1. Import packages and load data¶

In [4]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import sklearn
import xgboost as xgb
from xgboost import XGBClassifier

# import logistic regression libraries
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, ConfusionMatrixDisplay, roc_curve, roc_auc_score, RocCurveDisplay, auc

# import classification tree libraries
from sklearn.tree import DecisionTreeClassifier, plot_tree

# import random forest libraries
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import BaggingClassifier

# import cross validation libraries
from sklearn.model_selection import GridSearchCV, KFold
from sklearn.preprocessing import StandardScaler
In [5]:
# load data
df = pd.read_csv(r"C:\Users\woowe\Downloads\hospital_readmissions.csv")
print(df.head())
       age  time_in_hospital  n_lab_procedures  n_procedures  n_medications  \
0  [70-80)                 8                72             1             18   
1  [70-80)                 3                34             2             13   
2  [50-60)                 5                45             0             18   
3  [70-80)                 2                36             0             12   
4  [60-70)                 1                42             0              7   

   n_outpatient  n_inpatient  n_emergency medical_specialty       diag_1  \
0             2            0            0           Missing  Circulatory   
1             0            0            0             Other        Other   
2             0            0            0           Missing  Circulatory   
3             1            0            0           Missing  Circulatory   
4             0            0            0  InternalMedicine        Other   

        diag_2       diag_3 glucose_test A1Ctest change diabetes_med  \
0  Respiratory        Other           no      no     no          yes   
1        Other        Other           no      no     no          yes   
2  Circulatory  Circulatory           no      no    yes          yes   
3        Other     Diabetes           no      no    yes          yes   
4  Circulatory  Respiratory           no      no     no          yes   

  readmitted  
0         no  
1         no  
2        yes  
3        yes  
4         no  

Exploratory Data Analysis¶

Data quantity¶

In [8]:
print("The raw dataset has {} rows and {} columns".format(df.shape[0], df.shape[1]))
The raw dataset has 25000 rows and 17 columns

Null values¶

In [10]:
# check null values
missing_counts = df.isnull().sum()
print("Count of Missing Values")
print(missing_counts)
Count of Missing Values
age                  0
time_in_hospital     0
n_lab_procedures     0
n_procedures         0
n_medications        0
n_outpatient         0
n_inpatient          0
n_emergency          0
medical_specialty    0
diag_1               0
diag_2               0
diag_3               0
glucose_test         0
A1Ctest              0
change               0
diabetes_med         0
readmitted           0
dtype: int64

Data types¶

In [12]:
# check data types
df.dtypes
Out[12]:
age                  object
time_in_hospital      int64
n_lab_procedures      int64
n_procedures          int64
n_medications         int64
n_outpatient          int64
n_inpatient           int64
n_emergency           int64
medical_specialty    object
diag_1               object
diag_2               object
diag_3               object
glucose_test         object
A1Ctest              object
change               object
diabetes_med         object
readmitted           object
dtype: object

Univariate Distribution¶

In [14]:
# EDA on the entire raw dataset
# Define numeric and categorical columns for EDA
num_columns = df.select_dtypes(include=['int64']).columns.tolist()
cat_columns = df.select_dtypes(include=['object']).columns.tolist()

# Plot numeric feature distributions
plt.figure(figsize=(30, 15))
for i, column in enumerate(num_columns, 1):
    plt.subplot(4, 5, i)
    sns.histplot(df[column], kde=False)
    plt.title(column)
plt.tight_layout()
plt.show()

# Plot categorical feature distributions
plt.figure(figsize=(30, 5))
for i, column in enumerate(cat_columns, 1):
    plt.subplot(1, len(cat_columns), i)
    sns.countplot(x=column, data=df)
    plt.title(column)
plt.tight_layout()
plt.show()
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Summary table¶

In [16]:
# summary table of dataset
df.describe()
Out[16]:
time_in_hospital n_lab_procedures n_procedures n_medications n_outpatient n_inpatient n_emergency
count 25000.00000 25000.00000 25000.000000 25000.000000 25000.000000 25000.000000 25000.000000
mean 4.45332 43.24076 1.352360 16.252400 0.366400 0.615960 0.186600
std 3.00147 19.81862 1.715179 8.060532 1.195478 1.177951 0.885873
min 1.00000 1.00000 0.000000 1.000000 0.000000 0.000000 0.000000
25% 2.00000 31.00000 0.000000 11.000000 0.000000 0.000000 0.000000
50% 4.00000 44.00000 1.000000 15.000000 0.000000 0.000000 0.000000
75% 6.00000 57.00000 2.000000 20.000000 0.000000 1.000000 0.000000
max 14.00000 113.00000 6.000000 79.000000 33.000000 15.000000 64.000000

Check outliers¶

In [18]:
# check outliers
for column in df.select_dtypes(include=['number']).columns:
    plt.figure(figsize=(10, 6))  # Set figure size for each plot
    sns.boxplot(x=df[column])  # Create boxplot
    plt.title(f"Boxplot of {column}", fontdict={'fontsize': 20})  # Set title
    plt.xlabel(column)  # Label x-axis
    plt.show()  # Display the plot
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We noted that there were many outliers for n_lab_procedures, n_medications, n_outpatient, n_inpatient and n_emergency. However, since they are not data entry mistakes and are valid values, we decided to keep them.

2. Feature Engineering¶

Create interaction term 'severity'¶

  • The new column severity reflects the interaction between time spent in the hospital and the combined number of lab and medical procedures.
  • A higher value of severity could indicate a more serious condition, as it implies that patients with longer hospital stays and more procedures are likely to have greater health complexities.
  • The term 32 * df['n_procedures'] adds a weighted contribution of the number of procedures, suggesting that each procedure has a significant impact on the overall severity.
In [22]:
# create interaction term 'severity'
df_new = df.copy()
df_new['severity'] = df_new['time_in_hospital'] * (df_new['n_lab_procedures'] + 32 * df_new['n_procedures'])

Transform existing variables¶

  • For time_in_hospital which is right-skewed, we apply a regular natural log transform: The transformed variable is named log_time_in_hospital.

  • For n_procedures which is right-skewed, we apply a regular natural log transform: The transformed variable is named log_n_procedures.

  • For n_inpatient which is right-skewed, we apply a regular natural log transform: The transformed variable is named log_n_inpatient.

In [24]:
# Apply a log transform to existing variables
df_new["log_time_in_hospital"] = np.log1p(df_new["time_in_hospital"])
df_new["log_n_procedures"] = np.log1p(df_new["n_procedures"])
df_new["log_n_inpatient"] = np.log1p(df_new["n_inpatient"])

# Drop the original columns to prevent multicollinearity issues
df_new.drop(["time_in_hospital", "n_procedures", "n_inpatient"], axis = 1, inplace = True)
In [25]:
df_new.head()
Out[25]:
age n_lab_procedures n_medications n_outpatient n_emergency medical_specialty diag_1 diag_2 diag_3 glucose_test A1Ctest change diabetes_med readmitted severity log_time_in_hospital log_n_procedures log_n_inpatient
0 [70-80) 72 18 2 0 Missing Circulatory Respiratory Other no no no yes no 832 2.197225 0.693147 0.0
1 [70-80) 34 13 0 0 Other Other Other Other no no no yes no 294 1.386294 1.098612 0.0
2 [50-60) 45 18 0 0 Missing Circulatory Circulatory Circulatory no no yes yes yes 225 1.791759 0.000000 0.0
3 [70-80) 36 12 1 0 Missing Circulatory Other Diabetes no no yes yes yes 72 1.098612 0.000000 0.0
4 [60-70) 42 7 0 0 InternalMedicine Other Circulatory Respiratory no no no yes no 42 0.693147 0.000000 0.0

Exploratory Data Analysis (Processed Data)¶

Data types¶

In [28]:
# check data types
df_new.dtypes
Out[28]:
age                      object
n_lab_procedures          int64
n_medications             int64
n_outpatient              int64
n_emergency               int64
medical_specialty        object
diag_1                   object
diag_2                   object
diag_3                   object
glucose_test             object
A1Ctest                  object
change                   object
diabetes_med             object
readmitted               object
severity                  int64
log_time_in_hospital    float64
log_n_procedures        float64
log_n_inpatient         float64
dtype: object

Univariate Distribution¶

In [30]:
# EDA on the entire raw dataset
# Define numeric and categorical columns for EDA
num_columns_new = df_new.select_dtypes(include=['int64', 'float64']).columns.tolist()
cat_columns_new = df_new.select_dtypes(include=['object']).columns.tolist()

# Plot numeric feature distributions
plt.figure(figsize=(30, 15))
for i, column in enumerate(num_columns_new, 1):
    plt.subplot(4, 5, i)
    sns.histplot(df_new[column], kde=False)
    plt.title(column)
plt.tight_layout()
plt.show()

# Plot categorical feature distributions
plt.figure(figsize=(30, 5))
for i, column in enumerate(cat_columns_new, 1):
    plt.subplot(1, len(cat_columns_new), i)
    sns.countplot(x=column, data=df)
    plt.title(column)
plt.tight_layout()
plt.show()
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Summary table¶

In [32]:
# summary table of dataset
df_new.describe()
Out[32]:
n_lab_procedures n_medications n_outpatient n_emergency severity log_time_in_hospital log_n_procedures log_n_inpatient
count 25000.00000 25000.000000 25000.000000 25000.000000 25000.000000 25000.000000 25000.000000 25000.000000
mean 43.24076 16.252400 0.366400 0.186600 434.342320 1.553614 0.626528 0.323864
std 19.81862 8.060532 1.195478 0.885873 519.559753 0.535475 0.656202 0.500605
min 1.00000 1.000000 0.000000 0.000000 1.000000 0.693147 0.000000 0.000000
25% 31.00000 11.000000 0.000000 0.000000 116.000000 1.098612 0.000000 0.000000
50% 44.00000 15.000000 0.000000 0.000000 250.000000 1.609438 0.693147 0.000000
75% 57.00000 20.000000 0.000000 0.000000 537.500000 1.945910 1.098612 0.693147
max 113.00000 79.000000 33.000000 64.000000 4004.000000 2.708050 1.945910 2.772589

Check outliers¶

In [34]:
# check outliers
for column in df_new.select_dtypes(include=['number']).columns:
    plt.figure(figsize=(10, 6))  # Set figure size for each plot
    sns.boxplot(x=df_new[column])  # Create boxplot
    plt.title(f"Boxplot of {column}", fontdict={'fontsize': 20})  # Set title
    plt.xlabel(column)  # Label x-axis
    plt.show()  # Display the plot
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3. Data preprocessing¶

One-hot encoding¶

In [37]:
# one-hotting independent variables
df2 = pd.get_dummies(df_new, columns = ['diabetes_med'], drop_first = True, dtype = int)
df3 = pd.get_dummies(df2, columns = ['age'], drop_first = True, dtype = int)
df4 = pd.get_dummies(df3, columns = ['medical_specialty'], drop_first = True, dtype = int)
df5 = pd.get_dummies(df4, columns = ['diag_1'], drop_first = True, dtype = int)
df6 = pd.get_dummies(df5, columns = ['diag_2'], drop_first = True, dtype = int)
df7 = pd.get_dummies(df6, columns = ['diag_3'], drop_first = True, dtype = int)
df8 = pd.get_dummies(df7, columns = ['glucose_test'], drop_first = True, dtype = int)
df9 = pd.get_dummies(df8, columns = ['A1Ctest'], drop_first = True, dtype = int)
df10 = pd.get_dummies(df9, columns = ['change'], drop_first = True, dtype = int)

# one-hot readmission variable
df11 = pd.get_dummies(df10, columns = ['readmitted'], drop_first = True, dtype = int)
print(df11.head())
   n_lab_procedures  n_medications  n_outpatient  n_emergency  severity  \
0                72             18             2            0       832   
1                34             13             0            0       294   
2                45             18             0            0       225   
3                36             12             1            0        72   
4                42              7             0            0        42   

   log_time_in_hospital  log_n_procedures  log_n_inpatient  diabetes_med_yes  \
0              2.197225          0.693147              0.0                 1   
1              1.386294          1.098612              0.0                 1   
2              1.791759          0.000000              0.0                 1   
3              1.098612          0.000000              0.0                 1   
4              0.693147          0.000000              0.0                 1   

   age_[50-60)  ...  diag_3_Missing  diag_3_Musculoskeletal  diag_3_Other  \
0            0  ...               0                       0             1   
1            0  ...               0                       0             1   
2            1  ...               0                       0             0   
3            0  ...               0                       0             0   
4            0  ...               0                       0             0   

   diag_3_Respiratory  glucose_test_no  glucose_test_normal  A1Ctest_no  \
0                   0                1                    0           1   
1                   0                1                    0           1   
2                   0                1                    0           1   
3                   0                1                    0           1   
4                   1                1                    0           1   

   A1Ctest_normal  change_yes  readmitted_yes  
0               0           0               0  
1               0           0               0  
2               0           1               1  
3               0           1               1  
4               0           0               0  

[5 rows x 47 columns]

Train-test split¶

In [39]:
# define feature names for x and y datasets
# remove '[' for xgboost model to run successfully
y_name=['readmitted_yes']
x_name=['n_lab_procedures', 'n_medications', 'n_outpatient', 'n_emergency',
       'severity', 'log_time_in_hospital', 'log_n_procedures',
       'log_n_inpatient', 'diabetes_med_yes', 'age_50-60)', 'age_60-70)',
       'age_70-80)', 'age_80-90)', 'age_90-100)',
       'medical_specialty_Emergency/Trauma',
       'medical_specialty_Family/GeneralPractice',
       'medical_specialty_InternalMedicine', 'medical_specialty_Missing',
       'medical_specialty_Other', 'medical_specialty_Surgery',
       'diag_1_Diabetes', 'diag_1_Digestive', 'diag_1_Injury',
       'diag_1_Missing', 'diag_1_Musculoskeletal', 'diag_1_Other',
       'diag_1_Respiratory', 'diag_2_Diabetes', 'diag_2_Digestive',
       'diag_2_Injury', 'diag_2_Missing', 'diag_2_Musculoskeletal',
       'diag_2_Other', 'diag_2_Respiratory', 'diag_3_Diabetes',
       'diag_3_Digestive', 'diag_3_Injury', 'diag_3_Missing',
       'diag_3_Musculoskeletal', 'diag_3_Other', 'diag_3_Respiratory',
       'glucose_test_no', 'glucose_test_normal', 'A1Ctest_no',
       'A1Ctest_normal', 'change_yes']
In [40]:
# define features (X) and target (y)
df_X = df11.iloc[:, :-1] # drop 'readmitted_yes'
df_y = df11.iloc[:, -1:] # only store 'readmitted_yes'

# split data points (rows) into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(df_X, df_y, test_size = 0.2, random_state = 42)

# Scale the features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Convert scaled features back to DataFrame
df_X_train_scaled = pd.DataFrame(X_train_scaled, columns=x_name).reset_index(drop=True)
df_X_test_scaled = pd.DataFrame(X_test_scaled, columns=x_name).reset_index(drop=True)

df_y_train = pd.DataFrame(y_train, columns=y_name).reset_index(drop=True)
df_y_test = pd.DataFrame(y_test, columns=y_name).reset_index(drop=True)

df_train = pd.concat([df_X_train_scaled, df_y_train], axis = 1)
df_test = pd.concat([df_X_test_scaled, df_y_test], axis = 1)

To check any null values in final datasets

In [42]:
df_train.isnull().sum()
Out[42]:
n_lab_procedures                            0
n_medications                               0
n_outpatient                                0
n_emergency                                 0
severity                                    0
log_time_in_hospital                        0
log_n_procedures                            0
log_n_inpatient                             0
diabetes_med_yes                            0
age_50-60)                                  0
age_60-70)                                  0
age_70-80)                                  0
age_80-90)                                  0
age_90-100)                                 0
medical_specialty_Emergency/Trauma          0
medical_specialty_Family/GeneralPractice    0
medical_specialty_InternalMedicine          0
medical_specialty_Missing                   0
medical_specialty_Other                     0
medical_specialty_Surgery                   0
diag_1_Diabetes                             0
diag_1_Digestive                            0
diag_1_Injury                               0
diag_1_Missing                              0
diag_1_Musculoskeletal                      0
diag_1_Other                                0
diag_1_Respiratory                          0
diag_2_Diabetes                             0
diag_2_Digestive                            0
diag_2_Injury                               0
diag_2_Missing                              0
diag_2_Musculoskeletal                      0
diag_2_Other                                0
diag_2_Respiratory                          0
diag_3_Diabetes                             0
diag_3_Digestive                            0
diag_3_Injury                               0
diag_3_Missing                              0
diag_3_Musculoskeletal                      0
diag_3_Other                                0
diag_3_Respiratory                          0
glucose_test_no                             0
glucose_test_normal                         0
A1Ctest_no                                  0
A1Ctest_normal                              0
change_yes                                  0
readmitted_yes                              0
dtype: int64
In [43]:
df_test.isnull().sum()
Out[43]:
n_lab_procedures                            0
n_medications                               0
n_outpatient                                0
n_emergency                                 0
severity                                    0
log_time_in_hospital                        0
log_n_procedures                            0
log_n_inpatient                             0
diabetes_med_yes                            0
age_50-60)                                  0
age_60-70)                                  0
age_70-80)                                  0
age_80-90)                                  0
age_90-100)                                 0
medical_specialty_Emergency/Trauma          0
medical_specialty_Family/GeneralPractice    0
medical_specialty_InternalMedicine          0
medical_specialty_Missing                   0
medical_specialty_Other                     0
medical_specialty_Surgery                   0
diag_1_Diabetes                             0
diag_1_Digestive                            0
diag_1_Injury                               0
diag_1_Missing                              0
diag_1_Musculoskeletal                      0
diag_1_Other                                0
diag_1_Respiratory                          0
diag_2_Diabetes                             0
diag_2_Digestive                            0
diag_2_Injury                               0
diag_2_Missing                              0
diag_2_Musculoskeletal                      0
diag_2_Other                                0
diag_2_Respiratory                          0
diag_3_Diabetes                             0
diag_3_Digestive                            0
diag_3_Injury                               0
diag_3_Missing                              0
diag_3_Musculoskeletal                      0
diag_3_Other                                0
diag_3_Respiratory                          0
glucose_test_no                             0
glucose_test_normal                         0
A1Ctest_no                                  0
A1Ctest_normal                              0
change_yes                                  0
readmitted_yes                              0
dtype: int64

4. Model Training¶

K-fold cross validation is used since there is no class imbalance issue for our target variable.

In [46]:
# K-fold CV splitter
kf10 = KFold(n_splits = 10, shuffle = True, random_state = 42)
In [47]:
# Manually generate CV folds
def get_CV_folds(df, y, X, cv):
    train, val = [], []
    for train_index, val_index in cv.split(df[X], df[y]):
        train.append(df.loc[train_index])
        val.append(df.loc[val_index])
    return train, val
In [48]:
# Report GridSearchCV results
def report_GridSearchCV_results(grid):
    print("- Best combination of hyperparams:\n", grid.best_params_, "\n")
    print("- Best mean_test_score:\n", grid.best_score_, "\n")
    
    scores = []
    for i in range(grid.n_splits_):
        scores.append(grid.cv_results_['split{}_test_score'.format(i)][grid.best_index_])
    print("- Score by fold for best estimator:\n", scores, "\n")
    
    # View top 10 hyperparameter combinations by mean_test_score (mean AUC on validation set)
    print("- Top 10 hyperparameter combinations by mean_test_score:")
    display(pd.DataFrame(grid.cv_results_)[["rank_test_score", "mean_test_score"] 
                                            + ["param_" + param for param in grid.param_grid]]\
              .sort_values(by = "mean_test_score", ascending = False)\
              .set_index("rank_test_score").head(10))
    
    return None
In [49]:
# Compare training dataset performance vs validation dataset performance
def compare_performance(grid):  
    # retrieve training and validation scores
    train_scores=grid.cv_results_['mean_train_score']
    val_scores=grid.cv_results_['mean_test_score']

    # limit to 10 rows
    train_scores_limited=train_scores[:10]
    val_scores_limited=val_scores[:10]
    
    # create dataframe to store scores
    all_scores=pd.DataFrame({
    "train_AUC": train_scores_limited, 
    "val_AUC": val_scores_limited
    }, index=range(1,11))

    mean_scores=pd.DataFrame({
        "train_AUC": [train_scores_limited.mean()],
        "val_AUC": [val_scores_limited.mean()]
    }, index=["Mean"])

    all_scores_combined=pd.concat([all_scores, mean_scores])
    
    return all_scores_combined
In [50]:
# Plot histogram of SD of P(Readmitted) when training set varies
def plot_probability_std(estimator, df, y, X, cv, model_name):
    train, val = get_CV_folds(df, y, X, cv)
    test = val[-1]
    prob = pd.DataFrame() 
    
    for i in range(cv.n_splits - 1):
        train = val[i]
        estimator = estimator.fit(train[X], train[y])
        prob["Fold {}".format(i+1)] = [pred[1] for pred in estimator.predict_proba(test[X])]
        
    prob_std = prob.apply(lambda x: x.std(ddof=0), axis = 1)
    
    plt.figure(figsize = (8, 5))
    plt.hist(prob_std, rwidth = 0.6, bins = np.arange(0, 0.1, 0.01))
    plt.title("{} || SD of probability predictions when training set varies".format(model_name), fontsize = 14)
    plt.ylabel("Count of test observations", fontsize = 12)
    plt.xlabel("SD of P(Readmitted)", fontsize = 12)
    plt.show()
    
    return None
In [51]:
# Plot average feature importance across CV folds
def plot_avg_feature_importance(tree, df, y, X, cv, model_name):
    
    train, val = get_CV_folds(df, y, X, cv)
    impt = pd.DataFrame()
    
    for i in range(cv.n_splits):
        df_train = train[i]
        tree = tree.fit(df_train[X], df_train[y])
        impt[str(i)] = tree.feature_importances_
        
    ft = list(zip(X, impt.mean(axis = 1)))
    ft.sort(key = lambda x: x[1])
    plt.figure(figsize = (8, 10))
    features, importances = [x[0] for x in ft], [x[1] for x in ft]
    plt.barh(features, importances)
    plt.title("{} || Avg. feature importance across CV folds".format(model_name), fontsize = 14)
    plt.show()    

    return None
In [52]:
# evaluate model on test set
def evaluate_model(best_model, X_test_scaled, y_test): 
    """
    Parameters: 
    - best_model: The best estimator from grid search
    - X_test_scaled: Scaled test data
    - y_test: True labels for test set
    """
    # predict probabilities and labels
    y_prob=best_model.predict_proba(X_test_scaled)[:,1]
    y_pred=best_model.predict(X_test_scaled)

    # metrics
    test_auc=roc_auc_score(y_test, y_prob)
    accuracy=accuracy_score(y_test, y_pred)
    conf_matrix=confusion_matrix(y_test, y_pred)
    classification_rep=classification_report(y_test, y_pred)

    # print metrics
    print(f"Test AUC: {test_auc:.2f}")
    print(f'Accuracy: {accuracy:.2f}')
    print('Confusion Matrix:'); print(conf_matrix)
    disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)
    disp.plot()
    plt.show()
    print('Classification Report:')
    print(classification_rep)
In [53]:
# Plot ROC curve on test set
def plot_roc_curve(best_model, X_test_scaled, y_test):
    """
    Parameters:
    - best_model: The best estimator from grid search
    - X_test_scaled: Scaled test features
    - y_test: True labels for the test set
    """
    # predict probabilities
    y_prob=best_model.predict_proba(X_test_scaled)[:,1]

    # compute roc curve
    fpr, tpr, thresholds= roc_curve(y_test, y_prob)
    roc_auc=auc(fpr, tpr)

    # plot roc curve
    plt.figure(figsize=(8, 6))
    plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'AUC = {roc_auc:.2f}')
    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver Operating Characteristic (ROC) Curve')
    plt.legend(loc='lower right')
    plt.show()

Classification Tree¶

Pre-Pruning¶

In [54]:
# Initialize model
classificationtree = DecisionTreeClassifier(random_state=42)

# Define the hyperparameter grid
clf_param_grid = {
    'max_depth': [None, 2, 3, 4, 5],
    'min_samples_split': [2, 5, 10],
    'min_samples_leaf': [1, 2, 4],
    'max_leaf_nodes': [None, 5, 10, 15]
}

# Create a GridSearchCV object
grid_search_clf = GridSearchCV(estimator=classificationtree, param_grid=clf_param_grid, cv=kf10, scoring='roc_auc', verbose=4, return_train_score=True)

# Fit the GridSearchCV object to the training data
grid_search_clf.fit(df_train[x_name], df_train[y_name])
Fitting 10 folds for each of 180 candidates, totalling 1800 fits
[CV 1/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.535) total time=   0.3s
[CV 2/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.540) total time=   0.3s
[CV 3/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.518) total time=   0.3s
[CV 8/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 9/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.528) total time=   0.5s
[CV 10/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 1/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.993, test=0.531) total time=   0.3s
[CV 2/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.993, test=0.550) total time=   0.3s
[CV 3/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.994, test=0.556) total time=   0.3s
[CV 4/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.994, test=0.524) total time=   0.3s
[CV 5/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.994, test=0.537) total time=   0.4s
[CV 6/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 7/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.994, test=0.527) total time=   0.4s
[CV 8/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.994, test=0.546) total time=   0.3s
[CV 9/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.993, test=0.536) total time=   0.4s
[CV 10/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.994, test=0.537) total time=   0.3s
[CV 1/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.964, test=0.549) total time=   0.4s
[CV 2/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.962, test=0.562) total time=   0.2s
[CV 3/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.965, test=0.551) total time=   0.1s
[CV 4/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.963, test=0.536) total time=   0.2s
[CV 5/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.963, test=0.545) total time=   0.2s
[CV 6/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.963, test=0.542) total time=   0.2s
[CV 7/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.964, test=0.538) total time=   0.2s
[CV 8/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.965, test=0.550) total time=   0.3s
[CV 9/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.961, test=0.549) total time=   0.3s
[CV 10/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.962, test=0.542) total time=   0.7s
[CV 1/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.988, test=0.546) total time=   0.7s
[CV 2/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.989, test=0.566) total time=   0.9s
[CV 3/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.989, test=0.554) total time=   0.5s
[CV 4/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.988, test=0.529) total time=   0.5s
[CV 5/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.988, test=0.545) total time=   0.4s
[CV 6/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.988, test=0.548) total time=   0.4s
[CV 7/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.988, test=0.529) total time=   0.3s
[CV 8/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.988, test=0.552) total time=   0.4s
[CV 9/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.988, test=0.548) total time=   0.3s
[CV 10/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.988, test=0.544) total time=   0.4s
[CV 1/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.985, test=0.551) total time=   0.3s
[CV 2/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.984, test=0.557) total time=   0.2s
[CV 3/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.985, test=0.551) total time=   0.2s
[CV 4/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.984, test=0.532) total time=   0.1s
[CV 5/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.984, test=0.539) total time=   0.1s
[CV 6/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.984, test=0.553) total time=   0.1s
[CV 7/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 8/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.985, test=0.554) total time=   0.2s
[CV 9/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.984, test=0.555) total time=   0.2s
[CV 10/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.984, test=0.541) total time=   0.2s
[CV 1/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.953, test=0.551) total time=   0.1s
[CV 2/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.950, test=0.563) total time=   0.2s
[CV 3/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.953, test=0.554) total time=   0.2s
[CV 4/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.950, test=0.544) total time=   0.2s
[CV 5/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.951, test=0.553) total time=   0.3s
[CV 6/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.951, test=0.552) total time=   0.3s
[CV 7/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.954, test=0.547) total time=   0.3s
[CV 8/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.954, test=0.557) total time=   0.4s
[CV 9/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.949, test=0.556) total time=   0.3s
[CV 10/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.951, test=0.551) total time=   0.3s
[CV 1/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.938, test=0.551) total time=   0.3s
[CV 2/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.938, test=0.550) total time=   0.2s
[CV 3/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.940, test=0.576) total time=   0.2s
[CV 4/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.938, test=0.541) total time=   0.2s
[CV 5/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.938, test=0.561) total time=   0.2s
[CV 6/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.938, test=0.553) total time=   0.2s
[CV 7/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.940, test=0.554) total time=   0.2s
[CV 8/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.938, test=0.551) total time=   0.2s
[CV 9/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.936, test=0.570) total time=   0.2s
[CV 10/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.938, test=0.550) total time=   0.2s
[CV 1/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.938, test=0.551) total time=   0.2s
[CV 2/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.938, test=0.550) total time=   0.1s
[CV 3/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.940, test=0.576) total time=   0.3s
[CV 4/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.938, test=0.541) total time=   0.2s
[CV 5/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.938, test=0.561) total time=   0.1s
[CV 6/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.938, test=0.553) total time=   0.1s
[CV 7/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.940, test=0.554) total time=   0.1s
[CV 8/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.938, test=0.551) total time=   0.1s
[CV 9/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.936, test=0.570) total time=   0.2s
[CV 10/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.938, test=0.550) total time=   0.2s
[CV 1/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.928, test=0.560) total time=   0.2s
[CV 2/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.928, test=0.558) total time=   0.2s
[CV 3/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.930, test=0.574) total time=   0.2s
[CV 4/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.928, test=0.545) total time=   0.2s
[CV 5/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.927, test=0.563) total time=   0.2s
[CV 6/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.928, test=0.564) total time=   0.2s
[CV 7/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.930, test=0.560) total time=   0.2s
[CV 8/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.929, test=0.551) total time=   0.2s
[CV 9/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.926, test=0.567) total time=   0.1s
[CV 10/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.928, test=0.557) total time=   0.1s
[CV 1/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.638, test=0.644) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.640, test=0.617) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.637, test=0.651) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.638, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.641, test=0.614) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.638, test=0.629) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.638, test=0.644) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.640, test=0.617) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.637, test=0.651) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.638, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.641, test=0.614) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.638, test=0.629) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.638, test=0.644) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.640, test=0.617) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.637, test=0.651) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.638, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.641, test=0.614) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.638, test=0.629) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.638, test=0.644) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.640, test=0.617) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.637, test=0.651) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.638, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.641, test=0.614) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.638, test=0.629) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.638, test=0.644) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.640, test=0.617) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.637, test=0.651) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.638, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.641, test=0.614) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.638, test=0.629) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.638, test=0.644) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.640, test=0.617) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.637, test=0.651) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.638, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.641, test=0.614) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.638, test=0.629) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.638, test=0.644) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.640, test=0.617) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.637, test=0.651) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.638, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.641, test=0.614) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.638, test=0.629) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.638, test=0.644) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.640, test=0.617) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.637, test=0.651) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.638, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.641, test=0.614) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.638, test=0.629) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.638, test=0.644) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.640, test=0.617) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.637, test=0.651) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.638, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.641, test=0.614) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.638, test=0.629) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.643, test=0.643) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.641, test=0.638) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.641, test=0.636) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.647, test=0.621) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.641, test=0.647) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.644, test=0.654) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.645, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.645, test=0.635) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.644, test=0.631) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.643, test=0.643) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.641, test=0.638) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.641, test=0.636) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.647, test=0.621) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.641, test=0.647) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.644, test=0.654) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.645, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.645, test=0.635) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.644, test=0.631) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.643, test=0.643) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.641, test=0.638) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.641, test=0.636) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.647, test=0.621) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.641, test=0.647) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.644, test=0.654) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.645, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.645, test=0.635) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.644, test=0.631) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.643, test=0.643) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.641, test=0.638) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.641, test=0.636) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.647, test=0.621) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.641, test=0.647) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.644, test=0.654) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.645, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.645, test=0.635) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.644, test=0.631) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.643, test=0.643) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.641, test=0.638) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.641, test=0.636) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.647, test=0.621) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.641, test=0.647) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.644, test=0.654) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.645, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.645, test=0.635) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.644, test=0.631) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.643, test=0.643) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.641, test=0.638) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.641, test=0.636) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.647, test=0.621) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.641, test=0.647) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.644, test=0.654) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.645, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.645, test=0.635) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.644, test=0.631) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.643, test=0.643) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.641, test=0.638) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.641, test=0.636) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.647, test=0.621) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.641, test=0.647) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.644, test=0.654) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.645, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.645, test=0.635) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.644, test=0.631) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.643, test=0.643) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.641, test=0.638) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.641, test=0.636) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.647, test=0.621) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.641, test=0.647) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.644, test=0.654) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.645, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.645, test=0.635) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.644, test=0.631) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.643, test=0.643) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.641, test=0.638) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.641, test=0.636) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.647, test=0.621) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.641, test=0.647) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.644, test=0.654) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.645, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.645, test=0.635) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.644, test=0.631) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.630, test=0.610) total time=   0.1s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.634, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.637, test=0.623) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.634, test=0.638) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.639, test=0.604) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.634, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.637, test=0.623) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.634, test=0.638) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.639, test=0.604) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.634, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.637, test=0.623) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.634, test=0.638) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.639, test=0.604) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.634, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.637, test=0.623) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.634, test=0.638) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.639, test=0.604) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.634, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.637, test=0.623) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.634, test=0.638) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.639, test=0.604) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.634, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.637, test=0.623) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.634, test=0.638) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.639, test=0.604) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.634, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.637, test=0.623) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.634, test=0.638) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.639, test=0.604) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.634, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.637, test=0.623) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.634, test=0.638) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.639, test=0.604) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.634, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.637, test=0.623) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.634, test=0.638) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.639, test=0.604) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.632, test=0.638) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.634, test=0.616) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.632, test=0.645) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.632, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.636, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.633, test=0.628) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.633, test=0.632) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.632, test=0.638) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.634, test=0.616) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.632, test=0.645) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.632, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.636, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.633, test=0.628) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.633, test=0.632) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.632, test=0.638) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.634, test=0.616) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.632, test=0.645) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.632, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.636, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.633, test=0.628) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.633, test=0.632) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.632, test=0.638) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.634, test=0.616) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.632, test=0.645) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.632, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.636, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.633, test=0.628) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.633, test=0.632) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.632, test=0.638) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.634, test=0.616) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.632, test=0.645) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.632, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.636, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.633, test=0.628) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.633, test=0.632) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.632, test=0.638) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.634, test=0.616) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.632, test=0.645) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.632, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.636, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.633, test=0.628) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.633, test=0.632) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.632, test=0.638) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.634, test=0.616) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.632, test=0.645) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.632, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.636, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.633, test=0.628) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.633, test=0.632) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.632, test=0.638) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.634, test=0.616) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.632, test=0.645) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.632, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.636, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.633, test=0.628) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.633, test=0.632) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.632, test=0.638) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.634, test=0.616) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.632, test=0.645) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.632, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.636, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.633, test=0.628) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.633, test=0.632) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.634, test=0.640) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.637, test=0.622) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.634, test=0.639) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.639, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.634, test=0.640) total time=   0.1s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.637, test=0.622) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.634, test=0.639) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.639, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.634, test=0.640) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.637, test=0.622) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.634, test=0.639) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.639, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.634, test=0.640) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.637, test=0.622) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.634, test=0.639) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.639, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.634, test=0.640) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.637, test=0.622) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.634, test=0.639) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.639, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.634, test=0.640) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.637, test=0.622) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.634, test=0.639) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.639, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.634, test=0.640) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.637, test=0.622) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.634, test=0.639) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.639, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.634, test=0.640) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.637, test=0.622) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.634, test=0.639) total time=   0.1s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.639, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.634, test=0.640) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.637, test=0.622) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.634, test=0.639) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.639, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.643, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.644, test=0.626) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.642, test=0.640) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.645, test=0.613) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.646, test=0.643) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.644, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.647, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.647, test=0.616) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.643, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.644, test=0.626) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.642, test=0.640) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.645, test=0.613) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.646, test=0.643) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.644, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.647, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.647, test=0.616) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.643, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.644, test=0.626) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.642, test=0.640) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.645, test=0.613) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.646, test=0.643) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.644, test=0.640) total time=   0.1s
[CV 7/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.644, test=0.635) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.647, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.647, test=0.616) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.643, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.644, test=0.626) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.642, test=0.640) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.645, test=0.612) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.646, test=0.643) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.644, test=0.635) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.647, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.647, test=0.616) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.643, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.644, test=0.626) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.642, test=0.640) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.645, test=0.612) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.646, test=0.643) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.644, test=0.635) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.647, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.647, test=0.616) total time=   0.1s
[CV 10/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.643, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.644, test=0.626) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.642, test=0.640) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.645, test=0.612) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.646, test=0.643) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.644, test=0.635) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.647, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.647, test=0.616) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.643, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.644, test=0.626) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.642, test=0.640) total time=   0.1s
[CV 4/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.645, test=0.612) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.646, test=0.643) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.644, test=0.641) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.644, test=0.635) total time=   0.1s
[CV 8/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.647, test=0.610) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.647, test=0.617) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.643, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.644, test=0.626) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.642, test=0.640) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.645, test=0.612) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.646, test=0.643) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.644, test=0.641) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.644, test=0.635) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.647, test=0.610) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.647, test=0.617) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.643, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.644, test=0.626) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.642, test=0.640) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.645, test=0.612) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.646, test=0.643) total time=   0.1s
[CV 6/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.644, test=0.641) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.644, test=0.635) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.647, test=0.610) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.647, test=0.617) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.635, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.638, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.635, test=0.648) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.636, test=0.637) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.639, test=0.608) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.635, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.635, test=0.634) total time=   0.1s
[CV 3/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.638, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.635, test=0.648) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.636, test=0.637) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.639, test=0.608) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.635, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.638, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.635, test=0.648) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.636, test=0.637) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.639, test=0.608) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.635, test=0.642) total time=   0.1s
[CV 2/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.635, test=0.634) total time=   0.1s
[CV 3/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.638, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.637, test=0.642) total time=   0.1s
[CV 6/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.635, test=0.648) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.636, test=0.637) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.639, test=0.608) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.635, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.635, test=0.634) total time=   0.1s
[CV 3/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.638, test=0.618) total time=   0.1s
[CV 5/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.635, test=0.648) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.636, test=0.637) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.639, test=0.608) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.635, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.638, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.635, test=0.648) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.636, test=0.637) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.639, test=0.608) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.635, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.638, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.635, test=0.648) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.636, test=0.637) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.639, test=0.608) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.635, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.638, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.635, test=0.648) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.636, test=0.637) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.639, test=0.608) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.635, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.638, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.635, test=0.648) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.636, test=0.637) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.639, test=0.608) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.638, test=0.645) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.639, test=0.631) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.637, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.641, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.640, test=0.644) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.638, test=0.642) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.639, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.643, test=0.605) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.641, test=0.621) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.640, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.638, test=0.645) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.639, test=0.631) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.637, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.641, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.640, test=0.644) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.638, test=0.642) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.639, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.643, test=0.605) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.641, test=0.621) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.640, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.638, test=0.645) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.639, test=0.631) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.637, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.641, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.640, test=0.644) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.638, test=0.642) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.639, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.643, test=0.605) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.641, test=0.621) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.640, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.638, test=0.645) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.639, test=0.631) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.637, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.641, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.640, test=0.644) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.638, test=0.642) total time=   0.1s
[CV 7/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.639, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.643, test=0.605) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.641, test=0.621) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.640, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.638, test=0.645) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.639, test=0.631) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.637, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.641, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.640, test=0.644) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.638, test=0.642) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.639, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.643, test=0.605) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.641, test=0.621) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.640, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.638, test=0.645) total time=   0.1s
[CV 2/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.639, test=0.631) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.637, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.641, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.640, test=0.644) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.638, test=0.642) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.639, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.643, test=0.605) total time=   0.1s
[CV 9/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.641, test=0.621) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.640, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.638, test=0.645) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.639, test=0.631) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.637, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.641, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.640, test=0.644) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.638, test=0.642) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.639, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.643, test=0.605) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.641, test=0.621) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.640, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.638, test=0.645) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.639, test=0.631) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.637, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.641, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.640, test=0.644) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.638, test=0.642) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.639, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.643, test=0.605) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.641, test=0.621) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.640, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.638, test=0.645) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.639, test=0.631) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.637, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.641, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.640, test=0.644) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.638, test=0.642) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.639, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.643, test=0.605) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.641, test=0.621) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.640, test=0.633) total time=   0.0s
Out[54]:
GridSearchCV(cv=KFold(n_splits=10, random_state=42, shuffle=True),
             estimator=DecisionTreeClassifier(random_state=42),
             param_grid={'max_depth': [None, 2, 3, 4, 5],
                         'max_leaf_nodes': [None, 5, 10, 15],
                         'min_samples_leaf': [1, 2, 4],
                         'min_samples_split': [2, 5, 10]},
             return_train_score=True, scoring='roc_auc', verbose=4)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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GridSearchCV(cv=KFold(n_splits=10, random_state=42, shuffle=True),
             estimator=DecisionTreeClassifier(random_state=42),
             param_grid={'max_depth': [None, 2, 3, 4, 5],
                         'max_leaf_nodes': [None, 5, 10, 15],
                         'min_samples_leaf': [1, 2, 4],
                         'min_samples_split': [2, 5, 10]},
             return_train_score=True, scoring='roc_auc', verbose=4)
DecisionTreeClassifier(max_leaf_nodes=15, random_state=42)
DecisionTreeClassifier(max_leaf_nodes=15, random_state=42)

Model Performance¶

In [56]:
report_GridSearchCV_results(grid_search_clf)
- Best combination of hyperparams:
 {'max_depth': None, 'max_leaf_nodes': 15, 'min_samples_leaf': 1, 'min_samples_split': 2} 

- Best mean_test_score:
 0.6348561664387089 

- Score by fold for best estimator:
 [0.642826028833473, 0.6381912555990646, 0.6356443108525275, 0.6211688074352548, 0.6465267259472497, 0.6542109592126485, 0.6326661628391814, 0.6113999785603581, 0.6350141122190772, 0.6309133228882546] 

- Top 10 hyperparameter combinations by mean_test_score:
mean_test_score param_max_depth param_min_samples_split param_min_samples_leaf param_max_leaf_nodes
rank_test_score
1 0.634856 None 2 2 15
1 0.634856 None 5 1 15
1 0.634856 None 10 4 15
1 0.634856 None 5 4 15
1 0.634856 None 2 4 15
1 0.634856 None 10 2 15
1 0.634856 None 5 2 15
1 0.634856 None 10 1 15
1 0.634856 None 2 1 15
10 0.633061 None 5 2 10
In [57]:
compare_performance(grid_search_clf)
Out[57]:
train_AUC val_AUC
1 1.000000 0.533032
2 0.993662 0.537205
3 0.963196 0.546496
4 0.988201 0.546305
5 0.984342 0.547125
6 0.951628 0.552855
7 0.938195 0.555678
8 0.938195 0.555678
9 0.928294 0.559897
10 0.622964 0.621077
Mean 0.930868 0.555535
In [58]:
best_model_clf=grid_search_clf.best_estimator_
In [59]:
plot_probability_std(best_model_clf, df_train, y_name, x_name, kf10, "Pre-pruned Classification Tree")
No description has been provided for this image
In [60]:
plot_avg_feature_importance(best_model_clf, df_train, y_name, x_name, kf10, "Pre-pruned Classification Tree")
No description has been provided for this image
In [61]:
evaluate_model(best_model_clf, df_X_test_scaled, df_y_test)
Test AUC: 0.63
Accuracy: 0.60
Confusion Matrix:
[[1721  937]
 [1074 1268]]
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Classification Report:
              precision    recall  f1-score   support

           0       0.62      0.65      0.63      2658
           1       0.58      0.54      0.56      2342

    accuracy                           0.60      5000
   macro avg       0.60      0.59      0.59      5000
weighted avg       0.60      0.60      0.60      5000

In [62]:
plot_roc_curve(best_model_clf, df_X_test_scaled, df_y_test)
No description has been provided for this image
In [63]:
# Plotting the tree
plt.figure(figsize=(50, 20))
plot_tree(best_model_clf, filled=True, feature_names=x_name, class_names=['Not readmitted', 'Readmitted'], rounded=True)
plt.show()
No description has been provided for this image

Post-pruning¶

In [66]:
# Get effective alphas for pruning
path = classificationtree.cost_complexity_pruning_path(X_train_scaled, y_train)
ccp_alphas=path.ccp_alphas
impurities=path.impurities

# Define the hyperparameter grid
post_prune_param_grid = {
    'ccp_alpha': ccp_alphas
}

# Create a GridSearchCV object
grid_search_post_prune = GridSearchCV(estimator=classificationtree, param_grid=post_prune_param_grid, scoring='roc_auc', cv=kf10, verbose=4, return_train_score=True)

# Fit the GridSearchCV object to the training data
grid_search_post_prune.fit(df_train[x_name], df_train[y_name])
Fitting 10 folds for each of 2446 candidates, totalling 24460 fits
[CV 1/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.535) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.528) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.545) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.518) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.518) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.535) total time=   0.3s
[CV 2/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 4/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 4/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.528) total time=   0.3s
[CV 5/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 1/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 4/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.528) total time=   0.3s
[CV 10/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 1/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.518) total time=   0.3s
[CV 8/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.528) total time=   0.3s
[CV 10/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 9/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.545) total time=   0.4s
[CV 6/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.518) total time=   0.4s
[CV 7/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.518) total time=   0.3s
[CV 8/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.528) total time=   0.3s
[CV 10/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.535) total time=   0.3s
[CV 2/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.528) total time=   0.3s
[CV 5/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.545) total time=   0.3s
[CV 6/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.517) total time=   0.1s
[CV 7/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.517) total time=   0.1s
[CV 7/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.517) total time=   0.1s
[CV 7/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.517) total time=   0.1s
[CV 7/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.517) total time=   0.2s
[CV 7/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.517) total time=   0.1s
[CV 7/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.517) total time=   0.2s
[CV 7/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.517) total time=   0.1s
[CV 7/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.517) total time=   0.1s
[CV 7/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.517) total time=   0.2s
[CV 7/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 1/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.2s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.3s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.0s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.0s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.2s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.2s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.3s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.2s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.2s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.2s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 2/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 8/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.5s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.5s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.4s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.4s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.5s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.5s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.5s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.4s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.3s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.4s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.3s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.3s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.5s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.3s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.3s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.6s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.9s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   1.0s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.6s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.6s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.7s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.6s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.5s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.4s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.4s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.3s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.3s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.3s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.6s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.5s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.3s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 10/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.3s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.3s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.5s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.5s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 1/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 2/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 4/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 5/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.515) total time=   0.4s
[CV 7/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 8/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.6s
[CV 9/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 10/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 1/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 2/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.539) total time=   0.3s
[CV 3/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 5/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 6/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.515) total time=   0.3s
[CV 7/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.4s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.3s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.3s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.3s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.3s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.0s
[CV 10/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 2/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 1/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 10/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 2/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 2/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.543) total time=   0.2s
[CV 3/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 1/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 1/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.534) total time=   0.2s
[CV 2/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.2s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.511) total time=   0.2s
[CV 8/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.511) total time=   0.2s
[CV 8/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 1/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 6/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 6/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.511) total time=   0.2s
[CV 8/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 6/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.511) total time=   0.2s
[CV 8/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 1/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 10/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.511) total time=   0.2s
[CV 8/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 10/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 3/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.519) total time=   0.2s
[CV 7/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.532) total time=   0.2s
[CV 9/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.861111111111113e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=4.861111111111113e-05;, score=(train=0.999, test=0.546) total time=   0.1s
[CV 3/10] END ccp_alpha=4.861111111111113e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.861111111111113e-05;, score=(train=0.999, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=4.861111111111113e-05;, score=(train=0.999, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=4.861111111111113e-05;, score=(train=0.999, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=4.861111111111113e-05;, score=(train=1.000, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=4.861111111111113e-05;, score=(train=0.999, test=0.534) total time=   0.1s
[CV 9/10] END ccp_alpha=4.861111111111113e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.861111111111113e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=0.999, test=0.546) total time=   0.1s
[CV 3/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=0.999, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=0.999, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=0.999, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=1.000, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=0.999, test=0.534) total time=   0.1s
[CV 9/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=0.999, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=0.999, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=0.999, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=0.999, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=0.999, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=0.999, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.544) total time=   0.2s
[CV 3/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.2s
[CV 2/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.544) total time=   0.2s
[CV 3/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.2s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.2s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.3s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.2s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.2s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.2s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.2s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.2s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.999, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.998, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.999, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.999, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.999, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.999, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.999, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.999, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.998, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.999, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.999, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.999, test=0.510) total time=   0.2s
[CV 8/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.999, test=0.539) total time=   0.2s
[CV 9/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.999, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.998, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.998, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.998, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.998, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.998, test=0.543) total time=   0.1s
[CV 6/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.998, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.999, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.998, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.998, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.998, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.998, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.998, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.998, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.998, test=0.543) total time=   0.1s
[CV 6/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.998, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.998, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.998, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.998, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.998, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.998, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.998, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.998, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.998, test=0.543) total time=   0.1s
[CV 6/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.998, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.998, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.998, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.998, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.548) total time=   0.1s
[CV 4/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.548) total time=   0.1s
[CV 4/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.997, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.997, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.998, test=0.548) total time=   0.1s
[CV 4/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.997, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.997, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.997, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.997, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.997, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.997, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.997, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.996, test=0.544) total time=   0.2s
[CV 3/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.546) total time=   0.1s
[CV 4/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.996, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.996, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.996, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.996, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.546) total time=   0.1s
[CV 4/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.996, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.996, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.996, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.997, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.996, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.997, test=0.546) total time=   0.1s
[CV 4/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.997, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.996, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.996, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.997, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.997, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.996, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.997, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.996, test=0.539) total time=   0.2s
[CV 2/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.995, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.997, test=0.548) total time=   0.2s
[CV 4/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.996, test=0.534) total time=   0.1s
[CV 5/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.996, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.996, test=0.517) total time=   0.1s
[CV 7/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.996, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.996, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.996, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.996, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.996, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.996, test=0.548) total time=   0.1s
[CV 4/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.996, test=0.534) total time=   0.1s
[CV 5/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.996, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.996, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.996, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.996, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.996, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.996, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.996, test=0.548) total time=   0.2s
[CV 4/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.996, test=0.533) total time=   0.2s
[CV 5/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.996, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.996, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.996, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.996, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.995, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.996, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.996, test=0.546) total time=   0.1s
[CV 4/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.995, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.995, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.996, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.546) total time=   0.1s
[CV 4/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.546) total time=   0.1s
[CV 4/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.546) total time=   0.1s
[CV 4/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.546) total time=   0.2s
[CV 4/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.546) total time=   0.1s
[CV 4/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.543) total time=   0.2s
[CV 3/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.546) total time=   0.2s
[CV 4/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.546) total time=   0.1s
[CV 4/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.995, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.995, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.995, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.995, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.995, test=0.526) total time=   0.1s
[CV 1/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.994, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.995, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.995, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.995, test=0.524) total time=   0.1s
[CV 1/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.994, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.995, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.995, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.995, test=0.524) total time=   0.1s
[CV 1/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.994, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.995, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 6/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.995, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.995, test=0.524) total time=   0.1s
[CV 1/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.994, test=0.536) total time=   0.2s
[CV 2/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.994, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.995, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.995, test=0.514) total time=   0.1s
[CV 7/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.995, test=0.524) total time=   0.1s
[CV 1/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.995, test=0.514) total time=   0.1s
[CV 7/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.995, test=0.514) total time=   0.1s
[CV 7/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.995, test=0.514) total time=   0.1s
[CV 7/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.995, test=0.514) total time=   0.1s
[CV 7/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.995, test=0.514) total time=   0.1s
[CV 7/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.994, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.994, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.995, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.994, test=0.527) total time=   0.2s
[CV 6/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.994, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.994, test=0.499) total time=   0.1s
[CV 8/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.994, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.994, test=0.499) total time=   0.1s
[CV 8/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.994, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.994, test=0.499) total time=   0.1s
[CV 8/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.994, test=0.536) total time=   0.2s
[CV 9/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.544) total time=   0.2s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.544) total time=   0.2s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.994, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.994, test=0.535) total time=   0.2s
[CV 9/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.993, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.994, test=0.544) total time=   0.2s
[CV 4/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.994, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.993, test=0.534) total time=   0.2s
[CV 10/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.994, test=0.544) total time=   0.2s
[CV 4/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.994, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.534) total time=   0.2s
[CV 2/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.544) total time=   0.2s
[CV 4/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.995, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.535) total time=   0.2s
[CV 9/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.993, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.994, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.993, test=0.527) total time=   0.2s
[CV 6/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.993, test=0.533) total time=   0.2s
[CV 10/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.993, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.994, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.993, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.994, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.993, test=0.527) total time=   0.2s
[CV 6/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.535) total time=   0.2s
[CV 9/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 9/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 9/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.994, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 9/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 9/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.994, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.993, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.994, test=0.535) total time=   0.2s
[CV 9/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.994, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.993, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.994, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.535) total time=   0.2s
[CV 3/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.994, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.994, test=0.537) total time=   0.2s
[CV 9/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.2s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.2s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.2s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.2s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.2s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.2s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.2s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.2s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.2s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.2s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.993, test=0.534) total time=   0.2s
[CV 2/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.993, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.993, test=0.534) total time=   0.2s
[CV 2/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.993, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.537) total time=   0.2s
[CV 9/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.994, test=0.543) total time=   0.3s
[CV 4/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.992, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.994, test=0.543) total time=   0.2s
[CV 4/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.993, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.993, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.994, test=0.543) total time=   0.2s
[CV 4/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.993, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.993, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.993, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.993, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.993, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.993, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.993, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.993, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.993, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.993, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.993, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.994, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.993, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.994, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.993, test=0.542) total time=   0.2s
[CV 4/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.994, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.993, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.994, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.994, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.542) total time=   0.2s
[CV 4/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.994, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.993, test=0.542) total time=   0.2s
[CV 4/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.994, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.993, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.993, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.992, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.992, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.992, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.992, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.993, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.992, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.993, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.992, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.992, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.992, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.993, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.992, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.992, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.993, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.992, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.992, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.992, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.992, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.993, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.992, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.993, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.992, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.993, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.992, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.992, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.992, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.993, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.992, test=0.534) total time=   0.2s
[CV 10/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.992, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.992, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.992, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.993, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.993, test=0.537) total time=   0.2s
[CV 9/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.993, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.992, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.992, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.993, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.992, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.992, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.993, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.992, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.993, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.992, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.991, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.991, test=0.532) total time=   0.2s
[CV 3/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.993, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.992, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.992, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.992, test=0.537) total time=   0.2s
[CV 9/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.992, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.992, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.992, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.993, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.993, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.534) total time=   0.2s
[CV 2/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.535) total time=   0.2s
[CV 9/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.531) total time=   0.2s
[CV 6/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.532) total time=   0.2s
[CV 3/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.531) total time=   0.2s
[CV 6/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.534) total time=   0.2s
[CV 10/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.991, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.991, test=0.534) total time=   0.2s
[CV 10/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.992, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.535) total time=   0.2s
[CV 9/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.991, test=0.534) total time=   0.2s
[CV 10/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.992, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.992, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.992, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.991, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.991, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.991, test=0.510) total time=   0.1s
[CV 7/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.992, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.992, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.991, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.991, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.991, test=0.510) total time=   0.2s
[CV 7/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.992, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.992, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.991, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.991, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.991, test=0.510) total time=   0.1s
[CV 7/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.992, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.992, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.992, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.991, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.991, test=0.510) total time=   0.1s
[CV 7/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.991, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.992, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.992, test=0.536) total time=   0.2s
[CV 2/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.991, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.992, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.991, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.991, test=0.510) total time=   0.1s
[CV 7/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.991, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.992, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.992, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.991, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.991, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.992, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.992, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.991, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.991, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.992, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.992, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.991, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.991, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.992, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.992, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.991, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.991, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.992, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.992, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.990, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.991, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.991, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.992, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.991, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.992, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.992, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 9/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.992, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.992, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.3s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.3s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.3s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.3s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.3s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.3s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.3s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.2s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.2s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.2s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.2s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.2s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.2s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.2s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.990, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.990, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.990, test=0.531) total time=   0.2s
[CV 6/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.990, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.989, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.990, test=0.513) total time=   0.2s
[CV 7/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.990, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.990, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.990, test=0.531) total time=   0.2s
[CV 6/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.990, test=0.513) total time=   0.2s
[CV 7/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.990, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.990, test=0.531) total time=   0.2s
[CV 6/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 2/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.990, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.989, test=0.537) total time=   0.2s
[CV 9/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.990, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.990, test=0.531) total time=   0.2s
[CV 6/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.990, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 2/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.989, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.990, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.990, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.989, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.990, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.989, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.988, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.990, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.989, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.988, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.2s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.2s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.2s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.990, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.988, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.990, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.990, test=0.504) total time=   0.2s
[CV 8/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.988, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.990, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.988, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.988, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.988, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.988, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.989, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.988, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.989, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.988, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.990, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.987, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.989, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.989, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.987, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.989, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.989, test=0.538) total time=   0.2s
[CV 2/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.987, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.989, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.989, test=0.538) total time=   0.2s
[CV 2/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.987, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.989, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.987, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.989, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.989, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.988, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.987, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.989, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.989, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.989, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.988, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.987, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.989, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.989, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.988, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.987, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.989, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.988, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.987, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.988, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.988, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.987, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.988, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.989, test=0.519) total time=   0.2s
[CV 1/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.988, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.988, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.989, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.988, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.989, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.987, test=0.540) total time=   0.2s
[CV 9/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.513) total time=   0.2s
[CV 7/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.513) total time=   0.2s
[CV 7/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.540) total time=   0.2s
[CV 9/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.540) total time=   0.2s
[CV 2/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.519) total time=   0.2s
[CV 1/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.513) total time=   0.2s
[CV 7/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.540) total time=   0.2s
[CV 9/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.989, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.987, test=0.540) total time=   0.2s
[CV 9/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.989, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.987, test=0.540) total time=   0.2s
[CV 9/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.987, test=0.540) total time=   0.2s
[CV 9/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.986, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.987, test=0.540) total time=   0.2s
[CV 9/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.987, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.989, test=0.540) total time=   0.2s
[CV 2/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.986, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.987, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.989, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.988, test=0.513) total time=   0.2s
[CV 7/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.987, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.987, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.989, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.988, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.987, test=0.535) total time=   0.2s
[CV 3/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.989, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.988, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.989, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.987, test=0.540) total time=   0.2s
[CV 9/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.986, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.987, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.986, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.988, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.988, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.988, test=0.540) total time=   0.2s
[CV 2/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.987, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.989, test=0.537) total time=   0.2s
[CV 4/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.988, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.986, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.988, test=0.540) total time=   0.2s
[CV 2/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.987, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.986, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.987, test=0.535) total time=   0.2s
[CV 3/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.986, test=0.537) total time=   0.2s
[CV 10/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.988, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.989, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.986, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.986, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.988, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.989, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.988, test=0.540) total time=   0.2s
[CV 2/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.988, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.987, test=0.535) total time=   0.2s
[CV 3/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.985, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.987, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.985, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.987, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.985, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.988, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.986, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.987, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.985, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.986, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.985, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.989, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.986, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.985, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.986, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.988, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.985, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.539) total time=   0.3s
[CV 9/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.539) total time=   0.2s
[CV 2/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.987, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.537) total time=   0.2s
[CV 4/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.987, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.985, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.988, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.986, test=0.539) total time=   0.2s
[CV 9/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.988, test=0.539) total time=   0.2s
[CV 2/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.987, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.987, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.988, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.988, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.986, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.988, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.988, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.986, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.988, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.987, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.988, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.986, test=0.537) total time=   0.2s
[CV 10/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.988, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.988, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.988, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.988, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.988, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.985, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.988, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.988, test=0.539) total time=   0.2s
[CV 2/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.986, test=0.532) total time=   0.2s
[CV 3/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.985, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.988, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.985, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.988, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.985, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.988, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.986, test=0.532) total time=   0.2s
[CV 3/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.987, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.985, test=0.537) total time=   0.2s
[CV 10/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.987, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.985, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.537) total time=   0.2s
[CV 4/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.537) total time=   0.2s
[CV 4/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.988, test=0.537) total time=   0.2s
[CV 4/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.987, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.985, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.984, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.985, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.987, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.986, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.987, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.986, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.983, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.986, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.986, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.983, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.986, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.986, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.986, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.987, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.986, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.983, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.988, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.987, test=0.541) total time=   0.1s
[CV 2/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.985, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.987, test=0.541) total time=   0.1s
[CV 2/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.985, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.987, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.987, test=0.541) total time=   0.1s
[CV 2/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.985, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.986, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.984, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.986, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.987, test=0.541) total time=   0.1s
[CV 2/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.985, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.983, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.986, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.984, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.986, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.987, test=0.541) total time=   0.1s
[CV 2/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.985, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.987, test=0.536) total time=   0.1s
[CV 4/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.986, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.984, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.986, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.987, test=0.541) total time=   0.1s
[CV 2/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.985, test=0.530) total time=   0.2s
[CV 3/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.987, test=0.536) total time=   0.1s
[CV 4/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.986, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.987, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.984, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.986, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.980, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.982, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.980, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.980, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.981, test=0.524) total time=   0.1s
[CV 1/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.982, test=0.525) total time=   0.2s
[CV 6/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.982, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.980, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.981, test=0.524) total time=   0.1s
[CV 1/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 4/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.982, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.980, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.981, test=0.524) total time=   0.1s
[CV 1/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.982, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.980, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.981, test=0.524) total time=   0.1s
[CV 1/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.982, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.980, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.981, test=0.524) total time=   0.2s
[CV 1/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.982, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.981, test=0.524) total time=   0.1s
[CV 1/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.982, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 1/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.980, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.982, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 1/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.980, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.982, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 1/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.982, test=0.543) total time=   0.2s
[CV 2/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.980, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.981, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.983, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.979, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 1/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.981, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.983, test=0.500) total time=   0.2s
[CV 8/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.979, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 1/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 4/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.981, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.983, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.979, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 1/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.982, test=0.543) total time=   0.2s
[CV 2/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.982, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.983, test=0.500) total time=   0.2s
[CV 8/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.979, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.982, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.982, test=0.525) total time=   0.2s
[CV 6/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.983, test=0.500) total time=   0.2s
[CV 8/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.979, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.979, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.983, test=0.500) total time=   0.2s
[CV 8/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.979, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.3s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.3s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.981, test=0.524) total time=   0.2s
[CV 7/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.983, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 1/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.981, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.981, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.979, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.981, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.981, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.983, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.981, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.981, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.982, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.981, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.979, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.981, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.982, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.982, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.980, test=0.524) total time=   0.2s
[CV 7/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.981, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.981, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.981, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.980, test=0.524) total time=   0.2s
[CV 7/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.981, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.981, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.978, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.981, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.978, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.980, test=0.524) total time=   0.2s
[CV 7/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.981, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.978, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.981, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.980, test=0.524) total time=   0.2s
[CV 7/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.2s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.2s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.2s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.2s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.2s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.2s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.2s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.2s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.978, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.977, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.982, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.978, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.979, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.980, test=0.544) total time=   0.1s
[CV 2/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.978, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.980, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.980, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.982, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.977, test=0.542) total time=   0.2s
[CV 9/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.977, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.980, test=0.544) total time=   0.1s
[CV 2/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.978, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.977, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.977, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.980, test=0.544) total time=   0.1s
[CV 2/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.977, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.977, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.980, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.977, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.977, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.977, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.980, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.977, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.977, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.980, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.979, test=0.537) total time=   0.2s
[CV 4/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.977, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.977, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.979, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=7.6875e-05;, score=(train=0.980, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.6875e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.6875e-05;, score=(train=0.979, test=0.537) total time=   0.2s
[CV 4/10] END ccp_alpha=7.6875e-05;, score=(train=0.977, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.6875e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.6875e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.6875e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.6875e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.6875e-05;, score=(train=0.977, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.6875e-05;, score=(train=0.978, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.979, test=0.546) total time=   0.2s
[CV 2/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.977, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.981, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.977, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.977, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.978, test=0.519) total time=   0.2s
[CV 1/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.977, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.981, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.977, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.977, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.978, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.977, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.981, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.976, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.977, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.978, test=0.519) total time=   0.2s
[CV 1/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.977, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.981, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.976, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.978, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.977, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.976, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.978, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.978, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.981, test=0.504) total time=   0.2s
[CV 8/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.2s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.2s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.2s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.2s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.2s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.977, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.979, test=0.537) total time=   0.2s
[CV 4/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.976, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.976, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.547) total time=   0.1s
[CV 2/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.978, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.981, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.547) total time=   0.1s
[CV 2/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.981, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.547) total time=   0.1s
[CV 2/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.981, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.547) total time=   0.1s
[CV 2/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.981, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.547) total time=   0.2s
[CV 2/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.978, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.981, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.547) total time=   0.1s
[CV 2/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.981, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.547) total time=   0.1s
[CV 2/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.981, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.547) total time=   0.2s
[CV 2/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.981, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.547) total time=   0.1s
[CV 2/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.981, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.517) total time=   0.2s
[CV 1/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.979, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.978, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.979, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.980, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.977, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.978, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.978, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.978, test=0.532) total time=   0.1s
[CV 6/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.980, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.978, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.980, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.978, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.977, test=0.538) total time=   0.2s
[CV 3/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.978, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.8125e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.8125e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.8125e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.8125e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.8125e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.8125e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.8125e-05;, score=(train=0.980, test=0.504) total time=   0.2s
[CV 8/10] END ccp_alpha=7.8125e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.8125e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.8125e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.978, test=0.549) total time=   0.2s
[CV 2/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.977, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.977, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.978, test=0.549) total time=   0.2s
[CV 2/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.977, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.977, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.978, test=0.533) total time=   0.2s
[CV 6/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.976, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.975, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.975, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.976, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.975, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.974, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.976, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.975, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.976, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.976, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.978, test=0.533) total time=   0.2s
[CV 6/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.980, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.974, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.976, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.980, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.976, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.980, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.974, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.976, test=0.519) total time=   0.2s
[CV 1/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.976, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.977, test=0.532) total time=   0.2s
[CV 6/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.976, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.977, test=0.532) total time=   0.1s
[CV 6/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.979, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.974, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.976, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.977, test=0.532) total time=   0.1s
[CV 6/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.976, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.977, test=0.542) total time=   0.2s
[CV 4/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.977, test=0.532) total time=   0.1s
[CV 6/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.976, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.975, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.977, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.974, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.976, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.977, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.976, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.977, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.976, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.976, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.977, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.976, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.974, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.976, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.975, test=0.517) total time=   0.2s
[CV 1/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.977, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.976, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.977, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.976, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.976, test=0.535) total time=   0.2s
[CV 6/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.974, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.977, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.976, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.977, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.976, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.976, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.973, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.974, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.976, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.973, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.976, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.973, test=0.546) total time=   0.1s
[CV 9/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.974, test=0.515) total time=   0.1s
[CV 1/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.973, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.976, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.973, test=0.546) total time=   0.1s
[CV 9/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.974, test=0.515) total time=   0.1s
[CV 1/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.973, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.976, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.973, test=0.546) total time=   0.1s
[CV 9/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.974, test=0.515) total time=   0.1s
[CV 1/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.973, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.976, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.973, test=0.546) total time=   0.1s
[CV 9/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.974, test=0.515) total time=   0.1s
[CV 1/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.973, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.976, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.973, test=0.546) total time=   0.1s
[CV 9/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.974, test=0.515) total time=   0.1s
[CV 1/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.976, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.973, test=0.546) total time=   0.1s
[CV 9/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.974, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.973, test=0.546) total time=   0.1s
[CV 9/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.974, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.974, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.974, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.2s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.2s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.2s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.2s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.2s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.2s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.976, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.976, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.976, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.516) total time=   0.2s
[CV 1/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.976, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.976, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.976, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.975, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.972, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.972, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.973, test=0.517) total time=   0.2s
[CV 1/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.975, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.974, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.972, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.973, test=0.517) total time=   0.2s
[CV 1/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.975, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.972, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.973, test=0.517) total time=   0.2s
[CV 1/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.971, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.973, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.972, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.978, test=0.506) total time=   0.2s
[CV 8/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.973, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.972, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.973, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.974, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.978, test=0.506) total time=   0.2s
[CV 8/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.973, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.974, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.974, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.971, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.972, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.975, test=0.537) total time=   0.2s
[CV 6/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.974, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.971, test=0.540) total time=   0.2s
[CV 9/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.972, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.974, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.971, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.974, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.971, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.974, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.972, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.974, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.971, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.974, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.974, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.974, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.972, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.975, test=0.537) total time=   0.2s
[CV 6/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.974, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.972, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.975, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.972, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.972, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.975, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.972, test=0.522) total time=   0.2s
[CV 5/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.974, test=0.539) total time=   0.2s
[CV 6/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.972, test=0.542) total time=   0.2s
[CV 10/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.975, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.972, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.975, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.972, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.974, test=0.539) total time=   0.2s
[CV 6/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.977, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.975, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.973, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.972, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.975, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.972, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.974, test=0.539) total time=   0.2s
[CV 6/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.125e-05;, score=(train=0.975, test=0.551) total time=   0.2s
[CV 2/10] END ccp_alpha=8.125e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.125e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.125e-05;, score=(train=0.972, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.125e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.125e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.125e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.125e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.125e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.125e-05;, score=(train=0.971, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.125e-05;, score=(train=0.975, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.125e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.125e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.125e-05;, score=(train=0.972, test=0.522) total time=   0.2s
[CV 5/10] END ccp_alpha=8.125e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.125e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.125e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.125e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.125e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.125e-05;, score=(train=0.971, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.972, test=0.522) total time=   0.2s
[CV 5/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.971, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.972, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.977, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.971, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.974, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.972, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.971, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.972, test=0.522) total time=   0.2s
[CV 5/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.971, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.972, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.971, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.974, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.971, test=0.522) total time=   0.2s
[CV 5/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.972, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.976, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.971, test=0.519) total time=   0.2s
[CV 1/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.973, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.976, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.969, test=0.542) total time=   0.2s
[CV 9/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.971, test=0.519) total time=   0.2s
[CV 1/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.972, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.976, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.971, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.976, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.971, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.976, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.971, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.974, test=0.539) total time=   0.2s
[CV 6/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.976, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.971, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.974, test=0.539) total time=   0.2s
[CV 6/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.976, test=0.510) total time=   0.2s
[CV 8/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.969, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.971, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.974, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.974, test=0.539) total time=   0.2s
[CV 6/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.976, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.969, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.971, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.976, test=0.510) total time=   0.2s
[CV 8/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.971, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.971, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.972, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.976, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.971, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.971, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.972, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.976, test=0.510) total time=   0.2s
[CV 8/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.970, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.970, test=0.522) total time=   0.2s
[CV 5/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.976, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.970, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.972, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.976, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.969, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.970, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.976, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.970, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.972, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.976, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.971, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.970, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.975, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.971, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.970, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.974, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.972, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.975, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.969, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.970, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.971, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.975, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.968, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.969, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.970, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.973, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.971, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.975, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.968, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.970, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.969, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.970, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.973, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.975, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.968, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.970, test=0.542) total time=   0.2s
[CV 10/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.969, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.970, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.973, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.975, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.968, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.970, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.969, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.970, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.973, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.975, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.968, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.970, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.969, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.970, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.973, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.975, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.968, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.970, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.969, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.970, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.971, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.975, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.968, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.970, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.969, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.972, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.970, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.971, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.975, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.968, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.970, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.969, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.970, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.971, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.975, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.968, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.970, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.969, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.969, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.975, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.967, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.968, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=8.25e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.25e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.25e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=8.25e-05;, score=(train=0.969, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.25e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.25e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.25e-05;, score=(train=0.975, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.25e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.25e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.25e-05;, score=(train=0.968, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.972, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.972, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.969, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.972, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.971, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.975, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.968, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.972, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.969, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.975, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.968, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.972, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.969, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.971, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.975, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 9/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.968, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.972, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.971, test=0.542) total time=   0.2s
[CV 4/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.969, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.975, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.968, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.972, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.971, test=0.542) total time=   0.2s
[CV 4/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.969, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.975, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.968, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.972, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.971, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.969, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.975, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.968, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.972, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.971, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.969, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.971, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.970, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.974, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.969, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.968, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.972, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.971, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.969, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.971, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.974, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.969, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.968, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.972, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.971, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.969, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.971, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.974, test=0.507) total time=   0.2s
[CV 8/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.969, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.968, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.972, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.971, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.971, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.969, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.971, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.974, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.966, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.969, test=0.542) total time=   0.2s
[CV 10/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.968, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.972, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.971, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.971, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.969, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.971, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.969, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.974, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.966, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.968, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.972, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.971, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.970, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.968, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.971, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.969, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.974, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.966, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.968, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.972, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.971, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.970, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.968, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.971, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.969, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.974, test=0.507) total time=   0.2s
[CV 8/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.966, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.968, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.972, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.971, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.970, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.968, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.971, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.969, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.974, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.966, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.968, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.3s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.965, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.963, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.964, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.961, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.965, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.965, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.963, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.965, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.964, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.961, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.965, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.965, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.963, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.965, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.964, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.968, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.960, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.965, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.965, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.963, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.965, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.964, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.968, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.960, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.962, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.963, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.964, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.968, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.960, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.963, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.964, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.968, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.960, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.547) total time=   0.2s
[CV 4/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.963, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.964, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.968, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.960, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.963, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.964, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.968, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.960, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.964, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.964, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.963, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.964, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.964, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.968, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.960, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.963, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.962, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.964, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.964, test=0.548) total time=   0.1s
[CV 4/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.963, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.964, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.963, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.968, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.960, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.962, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.964, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.964, test=0.548) total time=   0.1s
[CV 4/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.963, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.964, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.963, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.968, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.960, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.962, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.964, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.964, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.963, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.964, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.963, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.968, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.960, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.962, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.964, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.964, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.963, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.964, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.963, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.968, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.960, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.962, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.964, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.964, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.963, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.964, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.963, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.968, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.960, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.962, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.964, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.964, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.963, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.963, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.962, test=0.544) total time=   0.2s
[CV 10/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.961, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.962, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.963, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.963, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.961, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.963, test=0.545) total time=   0.2s
[CV 3/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.963, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.963, test=0.524) total time=   0.2s
[CV 7/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.961, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.963, test=0.547) total time=   0.2s
[CV 4/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.963, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.963, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.967, test=0.507) total time=   0.2s
[CV 8/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.961, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.4375e-05;, score=(train=0.966, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=8.4375e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.4375e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.4375e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.4375e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.4375e-05;, score=(train=0.963, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.4375e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.4375e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.4375e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.4375e-05;, score=(train=0.961, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.966, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.963, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.961, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.966, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.962, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.963, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.961, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.966, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.963, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.959, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.962, test=0.545) total time=   0.2s
[CV 10/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.961, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.966, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.962, test=0.545) total time=   0.2s
[CV 10/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.960, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.966, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.962, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.960, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.966, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.963, test=0.547) total time=   0.2s
[CV 4/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.967, test=0.507) total time=   0.2s
[CV 8/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.960, test=0.529) total time=   0.2s
[CV 1/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.966, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.963, test=0.547) total time=   0.2s
[CV 4/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.960, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.966, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.960, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.966, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.963, test=0.545) total time=   0.2s
[CV 3/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.549) total time=   0.2s
[CV 6/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.967, test=0.507) total time=   0.2s
[CV 8/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.959, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.960, test=0.529) total time=   0.2s
[CV 1/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.966, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.963, test=0.547) total time=   0.2s
[CV 4/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.549) total time=   0.2s
[CV 6/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.960, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.966, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.959, test=0.546) total time=   0.1s
[CV 9/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.960, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.965, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.962, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.962, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.960, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.961, test=0.551) total time=   0.1s
[CV 6/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.966, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.957, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.961, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.959, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.965, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.962, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.962, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.960, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.961, test=0.551) total time=   0.1s
[CV 6/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.966, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.957, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.961, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.959, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.965, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.961, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.962, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.960, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.961, test=0.552) total time=   0.2s
[CV 6/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.962, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.966, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.957, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.961, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.959, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.965, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.961, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.962, test=0.549) total time=   0.2s
[CV 4/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.960, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.961, test=0.551) total time=   0.1s
[CV 6/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.961, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.965, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.957, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.961, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.959, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.964, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.961, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.962, test=0.549) total time=   0.2s
[CV 4/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.960, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.960, test=0.552) total time=   0.1s
[CV 6/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.961, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.965, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.957, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.960, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.959, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.964, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.961, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.960, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.960, test=0.552) total time=   0.1s
[CV 6/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.961, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.965, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.957, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.960, test=0.547) total time=   0.2s
[CV 10/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.959, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.964, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.961, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.962, test=0.549) total time=   0.2s
[CV 4/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.960, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.960, test=0.552) total time=   0.1s
[CV 6/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.961, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.965, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.956, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.960, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.959, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.964, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.961, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.961, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.960, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.959, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.960, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.964, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.956, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.960, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.959, test=0.529) total time=   0.2s
[CV 1/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.964, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.961, test=0.548) total time=   0.3s
[CV 3/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.961, test=0.549) total time=   0.2s
[CV 4/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.960, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.959, test=0.555) total time=   0.2s
[CV 6/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.960, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.964, test=0.508) total time=   0.3s
[CV 8/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.956, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.960, test=0.547) total time=   0.2s
[CV 10/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.959, test=0.529) total time=   0.3s
[CV 1/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.964, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.961, test=0.548) total time=   0.2s
[CV 3/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.961, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.960, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.959, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.960, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.964, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.956, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.960, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.959, test=0.529) total time=   0.2s
[CV 1/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.960, test=0.548) total time=   0.2s
[CV 3/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.960, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.964, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.956, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.960, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.960, test=0.548) total time=   0.2s
[CV 3/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.960, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.964, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.960, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.964, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.959, test=0.547) total time=   0.2s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.964, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.2s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.2s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.2s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.2s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.2s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.2s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.959, test=0.547) total time=   0.2s
[CV 10/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.963, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.956, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.956, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.955, test=0.544) total time=   0.3s
[CV 9/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.959, test=0.548) total time=   0.2s
[CV 10/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.963, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.955, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.959, test=0.548) total time=   0.2s
[CV 10/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.955, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.959, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.961, test=0.550) total time=   0.3s
[CV 4/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.963, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.955, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.959, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.963, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.955, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.959, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.963, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.955, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.959, test=0.548) total time=   0.2s
[CV 10/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.955, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.959, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.962, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.958, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.959, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.955, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.959, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.957, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.962, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.958, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.959, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.955, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.958, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.957, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.962, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.959, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.960, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.958, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.959, test=0.555) total time=   0.2s
[CV 6/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.959, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.962, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.954, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.958, test=0.549) total time=   0.1s
[CV 10/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.956, test=0.529) total time=   0.2s
[CV 1/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.962, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 3/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.960, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.957, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.959, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.959, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.962, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.954, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.958, test=0.549) total time=   0.1s
[CV 10/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.956, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.962, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 3/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.960, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.957, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.959, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.959, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.962, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.954, test=0.542) total time=   0.2s
[CV 9/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.958, test=0.549) total time=   0.2s
[CV 10/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.955, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.961, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 3/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.959, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.956, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.957, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.958, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.962, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.953, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 10/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.954, test=0.529) total time=   0.2s
[CV 1/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.961, test=0.551) total time=   0.2s
[CV 2/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 3/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.959, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.956, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.957, test=0.555) total time=   0.2s
[CV 6/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.958, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.962, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.953, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.957, test=0.551) total time=   0.2s
[CV 10/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.954, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.961, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 3/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.959, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.956, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.957, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.958, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.962, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.953, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 10/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.954, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.961, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 3/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.959, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.956, test=0.530) total time=   0.2s
[CV 5/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.957, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.958, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.962, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.953, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 10/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.954, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.961, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 3/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.959, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.956, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.957, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.958, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.961, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.953, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.957, test=0.552) total time=   0.1s
[CV 10/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.954, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.961, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 3/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.959, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.956, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.957, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.958, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.961, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.953, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.957, test=0.552) total time=   0.2s
[CV 10/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.954, test=0.529) total time=   0.2s
[CV 1/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.961, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 3/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.959, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.956, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.957, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.958, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.961, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.953, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.957, test=0.552) total time=   0.1s
[CV 10/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.954, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.961, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 3/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.959, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.956, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.957, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.958, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.961, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.953, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.957, test=0.552) total time=   0.1s
[CV 10/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.954, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.961, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 3/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.959, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.956, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.957, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.958, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.961, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.953, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.957, test=0.552) total time=   0.1s
[CV 10/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.954, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.961, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 3/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.959, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.956, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.956, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.958, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.961, test=0.507) total time=   0.2s
[CV 8/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.953, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 10/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.953, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.961, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.957, test=0.552) total time=   0.2s
[CV 3/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.956, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.956, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.957, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.960, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.955, test=0.553) total time=   0.1s
[CV 10/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.953, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.961, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.957, test=0.552) total time=   0.1s
[CV 3/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.956, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.956, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.957, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.960, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.955, test=0.553) total time=   0.2s
[CV 10/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.953, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.960, test=0.553) total time=   0.1s
[CV 2/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 3/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.959, test=0.549) total time=   0.2s
[CV 4/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.956, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.955, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.957, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.960, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.952, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 10/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.953, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.960, test=0.553) total time=   0.1s
[CV 2/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 3/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.956, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.955, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.957, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.960, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.952, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.955, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.953, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.960, test=0.553) total time=   0.1s
[CV 2/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.956, test=0.552) total time=   0.2s
[CV 3/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.956, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.955, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.957, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.960, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.952, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 3/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.955, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.955, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.957, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.960, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.952, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.960, test=0.554) total time=   0.2s
[CV 2/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 3/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.955, test=0.530) total time=   0.2s
[CV 5/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.955, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.957, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.952, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 3/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.959, test=0.549) total time=   0.2s
[CV 4/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.957, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.952, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 3/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.957, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.952, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 3/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.959, test=0.549) total time=   0.2s
[CV 4/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.957, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.952, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 3/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.530) total time=   0.2s
[CV 5/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.957, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.952, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.955, test=0.553) total time=   0.2s
[CV 3/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.956, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.955, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.956, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.952, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.956, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.952, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.960, test=0.554) total time=   0.2s
[CV 2/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.956, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.960, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.2s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.2s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.2s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.955, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.954, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.956, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.960, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.952, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.955, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.952, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.954, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.954, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.954, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.956, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.960, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.952, test=0.544) total time=   0.3s
[CV 9/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.954, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.954, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.954, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.954, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.956, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.952, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.954, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.952, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 2/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.954, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.954, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.952, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.954, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.959, test=0.555) total time=   0.2s
[CV 2/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.954, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.957, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.954, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.956, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.960, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.951, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.954, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.951, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.959, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.954, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.957, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.953, test=0.533) total time=   0.2s
[CV 5/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.954, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.955, test=0.524) total time=   0.2s
[CV 7/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.960, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.951, test=0.546) total time=   0.1s
[CV 9/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.954, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.951, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.958, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.953, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.953, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.953, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.955, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.959, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.951, test=0.547) total time=   0.1s
[CV 9/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.953, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.950, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.958, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.953, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.953, test=0.533) total time=   0.2s
[CV 5/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.953, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.955, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.959, test=0.510) total time=   0.2s
[CV 8/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.950, test=0.548) total time=   0.1s
[CV 9/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.953, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.950, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.958, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.953, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.956, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.953, test=0.534) total time=   0.1s
[CV 5/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.954, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.959, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.950, test=0.548) total time=   0.1s
[CV 9/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.953, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.949, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.958, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.953, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.956, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.953, test=0.534) total time=   0.1s
[CV 5/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.954, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.959, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.950, test=0.548) total time=   0.1s
[CV 9/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.953, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.949, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.958, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.952, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.956, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.952, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.953, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.954, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.958, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.950, test=0.548) total time=   0.1s
[CV 9/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.953, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.949, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.957, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.952, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.956, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.952, test=0.534) total time=   0.1s
[CV 5/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.954, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.957, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.950, test=0.548) total time=   0.1s
[CV 9/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.952, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.949, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.957, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.952, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.956, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.951, test=0.534) total time=   0.1s
[CV 5/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.953, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.953, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.957, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.950, test=0.548) total time=   0.1s
[CV 9/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.952, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.949, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.957, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.952, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.956, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.951, test=0.534) total time=   0.1s
[CV 5/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.953, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.957, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.950, test=0.548) total time=   0.1s
[CV 9/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.951, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.949, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.2s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.2s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.2s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.2s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.2s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.2s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.947, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.951, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.947, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.947, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.951, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.947, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.952, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.945, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.947, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.951, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.947, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.952, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.947, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.950, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.948, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.950, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.952, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.945, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.952, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.947, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.950, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.947, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.950, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.952, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.945, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.944, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.952, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.947, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.950, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.947, test=0.536) total time=   0.3s
[CV 5/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.947, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.952, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.945, test=0.556) total time=   0.2s
[CV 10/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.944, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.952, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.947, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.950, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.947, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.952, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.945, test=0.556) total time=   0.2s
[CV 10/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.952, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.946, test=0.555) total time=   0.3s
[CV 3/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.950, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.947, test=0.536) total time=   0.2s
[CV 5/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.947, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.949, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.952, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.945, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.944, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.952, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.946, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.950, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.947, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.947, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.949, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.952, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.945, test=0.556) total time=   0.2s
[CV 10/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.944, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.951, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.945, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.950, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.947, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.946, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.949, test=0.527) total time=   0.2s
[CV 7/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.952, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.944, test=0.551) total time=   0.1s
[CV 9/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.945, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.943, test=0.536) total time=   0.1s
[CV 1/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.951, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.945, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.950, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.946, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.946, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.949, test=0.527) total time=   0.1s
[CV 7/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.951, test=0.511) total time=   0.2s
[CV 8/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.944, test=0.551) total time=   0.1s
[CV 9/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.943, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.951, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.945, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.950, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.946, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.946, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.949, test=0.527) total time=   0.2s
[CV 7/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.951, test=0.511) total time=   0.2s
[CV 8/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.944, test=0.551) total time=   0.1s
[CV 9/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.943, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.950, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.945, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.950, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.946, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.946, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.947, test=0.528) total time=   0.2s
[CV 7/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.951, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.944, test=0.551) total time=   0.1s
[CV 9/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.943, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.950, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.945, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.950, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.946, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.946, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.947, test=0.528) total time=   0.1s
[CV 7/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.951, test=0.512) total time=   0.1s
[CV 8/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.944, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.943, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.950, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.945, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.950, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.946, test=0.535) total time=   0.2s
[CV 5/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.946, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.947, test=0.528) total time=   0.2s
[CV 7/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.951, test=0.512) total time=   0.2s
[CV 8/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.944, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.943, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.950, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.945, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.950, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.946, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.946, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.947, test=0.528) total time=   0.1s
[CV 7/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.951, test=0.512) total time=   0.2s
[CV 8/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.944, test=0.552) total time=   0.2s
[CV 9/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.943, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.950, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.945, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.950, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.946, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.946, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.947, test=0.528) total time=   0.1s
[CV 7/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.951, test=0.512) total time=   0.1s
[CV 8/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.944, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.943, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.950, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.945, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.950, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.946, test=0.535) total time=   0.2s
[CV 5/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.946, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.947, test=0.528) total time=   0.1s
[CV 7/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.951, test=0.512) total time=   0.2s
[CV 8/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.943, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.943, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.950, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.945, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.950, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.946, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.945, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.947, test=0.528) total time=   0.1s
[CV 7/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.951, test=0.512) total time=   0.1s
[CV 8/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.943, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.943, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.950, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.948, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.947, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.943, test=0.552) total time=   0.2s
[CV 9/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.942, test=0.537) total time=   0.2s
[CV 1/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.945, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.552) total time=   0.2s
[CV 9/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.945, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.945, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.537) total time=   0.2s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.2s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.2s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.2s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.2s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.2s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.2s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.2s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.2s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.2s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.2s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.2s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.2s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.2s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.2s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.2s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.2s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.2s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.2s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.2s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.2s
[CV 1/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.949, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.945, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.945, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.946, test=0.530) total time=   0.1s
[CV 7/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.949, test=0.513) total time=   0.2s
[CV 8/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.949, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.948, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.945, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.945, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.946, test=0.530) total time=   0.1s
[CV 7/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.945, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.945, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.946, test=0.530) total time=   0.1s
[CV 7/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.942, test=0.552) total time=   0.2s
[CV 9/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.948, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.942, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.948, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.945, test=0.536) total time=   0.2s
[CV 5/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.944, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.945, test=0.533) total time=   0.1s
[CV 7/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.949, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.942, test=0.553) total time=   0.2s
[CV 9/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.941, test=0.538) total time=   0.2s
[CV 1/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.948, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.942, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.948, test=0.555) total time=   0.2s
[CV 4/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.944, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.945, test=0.533) total time=   0.1s
[CV 7/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.949, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.941, test=0.555) total time=   0.2s
[CV 9/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.941, test=0.538) total time=   0.1s
[CV 1/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.948, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.942, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.948, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.944, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.945, test=0.533) total time=   0.2s
[CV 7/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.949, test=0.515) total time=   0.2s
[CV 8/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.941, test=0.555) total time=   0.1s
[CV 9/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.941, test=0.538) total time=   0.2s
[CV 1/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.948, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.942, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.948, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.944, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.945, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.949, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.941, test=0.555) total time=   0.1s
[CV 9/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.943, test=0.556) total time=   0.2s
[CV 10/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.941, test=0.538) total time=   0.1s
[CV 1/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.948, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.942, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.948, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.944, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.945, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.949, test=0.515) total time=   0.2s
[CV 8/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.941, test=0.555) total time=   0.2s
[CV 9/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.941, test=0.538) total time=   0.1s
[CV 1/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.948, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.942, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.948, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.944, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.945, test=0.534) total time=   0.2s
[CV 7/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.948, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.941, test=0.555) total time=   0.1s
[CV 9/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.941, test=0.538) total time=   0.2s
[CV 1/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.947, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.941, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.947, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.943, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 7/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.948, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.940, test=0.555) total time=   0.1s
[CV 9/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.941, test=0.539) total time=   0.1s
[CV 1/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.947, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.941, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.947, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.943, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 7/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.948, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.940, test=0.554) total time=   0.2s
[CV 9/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.941, test=0.539) total time=   0.2s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.2s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.2s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.2s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.2s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.2s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.2s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.2s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.2s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.2s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.2s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.2s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.2s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.2s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.946, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.946, test=0.517) total time=   0.2s
[CV 8/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.940, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.942, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 7/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.938, test=0.555) total time=   0.1s
[CV 9/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.942, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.944, test=0.535) total time=   0.2s
[CV 7/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.938, test=0.555) total time=   0.1s
[CV 9/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.945, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.940, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.942, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.943, test=0.535) total time=   0.1s
[CV 7/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.938, test=0.555) total time=   0.1s
[CV 9/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.945, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.939, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.945, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.942, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.942, test=0.534) total time=   0.2s
[CV 7/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.938, test=0.541) total time=   0.2s
[CV 1/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.945, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.939, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.945, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.942, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.941, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.945, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.939, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.945, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 5/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.945, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.939, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.944, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.942, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.942, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.946, test=0.517) total time=   0.2s
[CV 8/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.938, test=0.556) total time=   0.2s
[CV 9/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.938, test=0.541) total time=   0.2s
[CV 1/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.945, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.944, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.942, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.941, test=0.533) total time=   0.2s
[CV 5/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.946, test=0.517) total time=   0.2s
[CV 8/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.944, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.938, test=0.541) total time=   0.2s
[CV 1/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.939, test=0.557) total time=   0.2s
[CV 3/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.946, test=0.517) total time=   0.2s
[CV 8/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.941, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.939, test=0.557) total time=   0.2s
[CV 3/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.939, test=0.557) total time=   0.2s
[CV 3/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.944, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.942, test=0.534) total time=   0.2s
[CV 7/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.944, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.942, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.946, test=0.517) total time=   0.2s
[CV 8/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.938, test=0.556) total time=   0.2s
[CV 9/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.941, test=0.533) total time=   0.2s
[CV 5/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.946, test=0.517) total time=   0.2s
[CV 8/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.944, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.942, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.944, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.941, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.941, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.937, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.937, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.944, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.944, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.941, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.941, test=0.535) total time=   0.1s
[CV 7/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.937, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.940, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.937, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.939, test=0.557) total time=   0.2s
[CV 3/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.944, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.941, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.941, test=0.535) total time=   0.1s
[CV 7/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.937, test=0.556) total time=   0.2s
[CV 9/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.940, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.937, test=0.541) total time=   0.2s
[CV 1/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.944, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.941, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.941, test=0.535) total time=   0.1s
[CV 7/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.937, test=0.556) total time=   0.2s
[CV 9/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.940, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.937, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.943, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.941, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.941, test=0.535) total time=   0.1s
[CV 7/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.946, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.937, test=0.558) total time=   0.1s
[CV 9/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.937, test=0.543) total time=   0.2s
[CV 1/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.943, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.940, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.941, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.941, test=0.535) total time=   0.1s
[CV 7/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.945, test=0.520) total time=   0.1s
[CV 8/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.936, test=0.559) total time=   0.2s
[CV 9/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.940, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.937, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.943, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.937, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.942, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.939, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.940, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.940, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.944, test=0.524) total time=   0.1s
[CV 8/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.935, test=0.558) total time=   0.1s
[CV 9/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.939, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.936, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.943, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.937, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.942, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.939, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.940, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.940, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.944, test=0.524) total time=   0.1s
[CV 8/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.935, test=0.558) total time=   0.1s
[CV 9/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.939, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.936, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.937, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.942, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.940, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.525) total time=   0.1s
[CV 8/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.558) total time=   0.2s
[CV 9/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.938, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.937, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.942, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.940, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.525) total time=   0.1s
[CV 8/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.558) total time=   0.2s
[CV 9/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.938, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.937, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.942, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.940, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.525) total time=   0.1s
[CV 8/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.558) total time=   0.1s
[CV 9/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.938, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.937, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.942, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.940, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.525) total time=   0.1s
[CV 8/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.558) total time=   0.1s
[CV 9/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.938, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.937, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.942, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.940, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.525) total time=   0.2s
[CV 8/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.558) total time=   0.2s
[CV 9/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.938, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.942, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.937, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.942, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.939, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.939, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.939, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.943, test=0.525) total time=   0.2s
[CV 8/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.934, test=0.558) total time=   0.2s
[CV 9/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.938, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.935, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.942, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.937, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.941, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.939, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.939, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.939, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.943, test=0.525) total time=   0.2s
[CV 8/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.934, test=0.558) total time=   0.1s
[CV 9/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.938, test=0.556) total time=   0.2s
[CV 10/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.935, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.941, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.935, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.940, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.938, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.937, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.938, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.942, test=0.523) total time=   0.2s
[CV 8/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.933, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.937, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.934, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.933, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.936, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.935, test=0.539) total time=   0.1s
[CV 5/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.934, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.934, test=0.537) total time=   0.1s
[CV 7/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.939, test=0.525) total time=   0.1s
[CV 8/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.930, test=0.558) total time=   0.2s
[CV 9/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.933, test=0.559) total time=   0.1s
[CV 10/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.930, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.938, test=0.557) total time=   0.2s
[CV 2/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.933, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.936, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.935, test=0.539) total time=   0.1s
[CV 5/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.934, test=0.563) total time=   0.2s
[CV 6/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.934, test=0.537) total time=   0.1s
[CV 7/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.939, test=0.525) total time=   0.1s
[CV 8/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.930, test=0.558) total time=   0.1s
[CV 9/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.933, test=0.559) total time=   0.2s
[CV 10/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.932, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.936, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.934, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.934, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.934, test=0.537) total time=   0.1s
[CV 7/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.938, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.930, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.933, test=0.559) total time=   0.1s
[CV 10/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.932, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.936, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.934, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.933, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.934, test=0.537) total time=   0.1s
[CV 7/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.938, test=0.526) total time=   0.2s
[CV 8/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.930, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.932, test=0.560) total time=   0.1s
[CV 10/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.930, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.938, test=0.557) total time=   0.2s
[CV 2/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.932, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.936, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.934, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.933, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.934, test=0.537) total time=   0.2s
[CV 7/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.938, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.930, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.932, test=0.560) total time=   0.1s
[CV 10/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.930, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.932, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.936, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.934, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.933, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.934, test=0.537) total time=   0.2s
[CV 7/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.938, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.930, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.932, test=0.560) total time=   0.1s
[CV 10/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.932, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.936, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.934, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.933, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.933, test=0.537) total time=   0.1s
[CV 7/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.938, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.930, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.932, test=0.560) total time=   0.1s
[CV 10/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.932, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.936, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.934, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.933, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.933, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.938, test=0.526) total time=   0.2s
[CV 8/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.929, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.932, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.936, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.934, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.933, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.933, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.938, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.929, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.932, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.936, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.934, test=0.538) total time=   0.2s
[CV 5/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.932, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.933, test=0.538) total time=   0.2s
[CV 7/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.938, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.929, test=0.560) total time=   0.2s
[CV 9/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.932, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.935, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.934, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.932, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.933, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.938, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.929, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.932, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.935, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.934, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.932, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.933, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.938, test=0.526) total time=   0.2s
[CV 8/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.929, test=0.560) total time=   0.2s
[CV 9/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.930, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.937, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.935, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.933, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.932, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.937, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.937, test=0.557) total time=   0.2s
[CV 2/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.935, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.933, test=0.538) total time=   0.2s
[CV 5/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.932, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.937, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.930, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.937, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.935, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.933, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.932, test=0.538) total time=   0.2s
[CV 7/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.937, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.937, test=0.557) total time=   0.2s
[CV 2/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.935, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.933, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.932, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.937, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.937, test=0.557) total time=   0.2s
[CV 2/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.931, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.935, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.933, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.932, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.937, test=0.526) total time=   0.2s
[CV 8/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.931, test=0.562) total time=   0.1s
[CV 10/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.930, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.937, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.931, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.934, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.932, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.932, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.937, test=0.526) total time=   0.2s
[CV 8/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.929, test=0.559) total time=   0.2s
[CV 9/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.929, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.929, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.375e-05;, score=(train=0.937, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.375e-05;, score=(train=0.930, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.375e-05;, score=(train=0.934, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.375e-05;, score=(train=0.932, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.375e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=9.375e-05;, score=(train=0.932, test=0.538) total time=   0.2s
[CV 7/10] END ccp_alpha=9.375e-05;, score=(train=0.937, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.375e-05;, score=(train=0.929, test=0.559) total time=   0.2s
[CV 9/10] END ccp_alpha=9.375e-05;, score=(train=0.929, test=0.562) total time=   0.1s
[CV 10/10] END ccp_alpha=9.375e-05;, score=(train=0.929, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.375e-05;, score=(train=0.937, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.375e-05;, score=(train=0.930, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.375e-05;, score=(train=0.934, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.375e-05;, score=(train=0.932, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.375e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=9.375e-05;, score=(train=0.932, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.375e-05;, score=(train=0.937, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.375e-05;, score=(train=0.929, test=0.559) total time=   0.2s
[CV 9/10] END ccp_alpha=9.375e-05;, score=(train=0.929, test=0.562) total time=   0.1s
[CV 10/10] END ccp_alpha=9.375e-05;, score=(train=0.929, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.375e-05;, score=(train=0.937, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.375e-05;, score=(train=0.930, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.375e-05;, score=(train=0.934, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.375e-05;, score=(train=0.932, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.375e-05;, score=(train=0.931, test=0.561) total time=   0.2s
[CV 6/10] END ccp_alpha=9.375e-05;, score=(train=0.932, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.375e-05;, score=(train=0.937, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.375e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.375e-05;, score=(train=0.929, test=0.562) total time=   0.1s
[CV 10/10] END ccp_alpha=9.375e-05;, score=(train=0.929, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.937, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.930, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.934, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.932, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.932, test=0.537) total time=   0.1s
[CV 7/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.937, test=0.527) total time=   0.1s
[CV 8/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.928, test=0.562) total time=   0.1s
[CV 10/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.929, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.936, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.930, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.934, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.932, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.930, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.932, test=0.537) total time=   0.1s
[CV 7/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.937, test=0.527) total time=   0.1s
[CV 8/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.928, test=0.559) total time=   0.2s
[CV 9/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.927, test=0.563) total time=   0.1s
[CV 10/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.929, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.935, test=0.556) total time=   0.1s
[CV 2/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.928, test=0.558) total time=   0.2s
[CV 3/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.933, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.931, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.930, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.930, test=0.541) total time=   0.1s
[CV 7/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.935, test=0.526) total time=   0.2s
[CV 8/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.928, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.926, test=0.564) total time=   0.2s
[CV 10/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.928, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.935, test=0.556) total time=   0.2s
[CV 2/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.928, test=0.558) total time=   0.1s
[CV 3/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.932, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.931, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.930, test=0.541) total time=   0.2s
[CV 7/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.935, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.928, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.925, test=0.564) total time=   0.1s
[CV 10/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.928, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.935, test=0.556) total time=   0.1s
[CV 2/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.928, test=0.558) total time=   0.1s
[CV 3/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.931, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.931, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.930, test=0.541) total time=   0.1s
[CV 7/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.935, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.928, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.925, test=0.564) total time=   0.1s
[CV 10/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.927, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.935, test=0.556) total time=   0.1s
[CV 2/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.928, test=0.558) total time=   0.1s
[CV 3/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.931, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.931, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.930, test=0.541) total time=   0.1s
[CV 7/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.935, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.927, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.925, test=0.564) total time=   0.1s
[CV 10/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.927, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.934, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.928, test=0.558) total time=   0.2s
[CV 3/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.931, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.931, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.930, test=0.540) total time=   0.1s
[CV 7/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.934, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.927, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.924, test=0.564) total time=   0.1s
[CV 10/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.927, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.934, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.928, test=0.558) total time=   0.2s
[CV 3/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.931, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.931, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.930, test=0.540) total time=   0.1s
[CV 7/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.934, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.927, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.924, test=0.564) total time=   0.2s
[CV 10/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.927, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.934, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.928, test=0.558) total time=   0.1s
[CV 3/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.931, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.931, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.930, test=0.540) total time=   0.1s
[CV 7/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.934, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.927, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.924, test=0.564) total time=   0.1s
[CV 10/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.927, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.934, test=0.556) total time=   0.1s
[CV 2/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.928, test=0.558) total time=   0.1s
[CV 3/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.930, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.931, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.929, test=0.541) total time=   0.1s
[CV 7/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.934, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.927, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.924, test=0.564) total time=   0.2s
[CV 10/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.927, test=0.543) total time=   0.2s
[CV 1/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.934, test=0.556) total time=   0.1s
[CV 2/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.928, test=0.558) total time=   0.1s
[CV 3/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.930, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.931, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.928, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.929, test=0.541) total time=   0.1s
[CV 7/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.934, test=0.526) total time=   0.2s
[CV 8/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.927, test=0.560) total time=   0.2s
[CV 9/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.924, test=0.564) total time=   0.1s
[CV 10/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.927, test=0.543) total time=   0.2s
[CV 1/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.933, test=0.556) total time=   0.1s
[CV 2/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.928, test=0.558) total time=   0.1s
[CV 3/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.929, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.931, test=0.539) total time=   0.1s
[CV 5/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.928, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.929, test=0.541) total time=   0.1s
[CV 7/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.934, test=0.527) total time=   0.1s
[CV 8/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.926, test=0.561) total time=   0.1s
[CV 9/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.924, test=0.564) total time=   0.1s
[CV 10/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.926, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.933, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.927, test=0.558) total time=   0.1s
[CV 3/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.929, test=0.555) total time=   0.2s
[CV 4/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.931, test=0.539) total time=   0.1s
[CV 5/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.928, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.929, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.934, test=0.527) total time=   0.1s
[CV 8/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.926, test=0.561) total time=   0.1s
[CV 9/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.923, test=0.564) total time=   0.1s
[CV 10/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.926, test=0.543) total time=   0.2s
[CV 1/10] END ccp_alpha=9.5e-05;, score=(train=0.933, test=0.557) total time=   0.2s
[CV 2/10] END ccp_alpha=9.5e-05;, score=(train=0.927, test=0.558) total time=   0.2s
[CV 3/10] END ccp_alpha=9.5e-05;, score=(train=0.929, test=0.555) total time=   0.2s
[CV 4/10] END ccp_alpha=9.5e-05;, score=(train=0.931, test=0.539) total time=   0.2s
[CV 5/10] END ccp_alpha=9.5e-05;, score=(train=0.928, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=9.5e-05;, score=(train=0.929, test=0.542) total time=   0.2s
[CV 7/10] END ccp_alpha=9.5e-05;, score=(train=0.934, test=0.527) total time=   0.1s
[CV 8/10] END ccp_alpha=9.5e-05;, score=(train=0.926, test=0.561) total time=   0.2s
[CV 9/10] END ccp_alpha=9.5e-05;, score=(train=0.923, test=0.564) total time=   0.2s
[CV 10/10] END ccp_alpha=9.5e-05;, score=(train=0.926, test=0.543) total time=   0.3s
[CV 1/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.933, test=0.557) total time=   0.2s
[CV 2/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.927, test=0.558) total time=   0.2s
[CV 3/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.928, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.930, test=0.539) total time=   0.1s
[CV 5/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.928, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.928, test=0.542) total time=   0.2s
[CV 7/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.933, test=0.527) total time=   0.2s
[CV 8/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.926, test=0.562) total time=   0.1s
[CV 9/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.923, test=0.565) total time=   0.3s
[CV 10/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.926, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.929, test=0.563) total time=   0.2s
[CV 2/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.924, test=0.562) total time=   0.2s
[CV 3/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.926, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.927, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.925, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.925, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.930, test=0.530) total time=   0.2s
[CV 8/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.924, test=0.563) total time=   0.2s
[CV 9/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.919, test=0.565) total time=   0.2s
[CV 10/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.922, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.929, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.923, test=0.562) total time=   0.2s
[CV 3/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.926, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.927, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.924, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.924, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.928, test=0.532) total time=   0.2s
[CV 8/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.924, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.919, test=0.566) total time=   0.1s
[CV 10/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.922, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.929, test=0.562) total time=   0.2s
[CV 2/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.923, test=0.562) total time=   0.2s
[CV 3/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.926, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.927, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.924, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.924, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.928, test=0.532) total time=   0.1s
[CV 8/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.924, test=0.564) total time=   0.2s
[CV 9/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.919, test=0.566) total time=   0.1s
[CV 10/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.922, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.928, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.923, test=0.561) total time=   0.1s
[CV 3/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.925, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.926, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.924, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.924, test=0.542) total time=   0.2s
[CV 7/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.927, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.923, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.918, test=0.567) total time=   0.2s
[CV 10/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.921, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.928, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.923, test=0.561) total time=   0.1s
[CV 3/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.925, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.926, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.924, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.924, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.927, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.923, test=0.565) total time=   0.2s
[CV 9/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.918, test=0.567) total time=   0.2s
[CV 10/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.921, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.927, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.923, test=0.561) total time=   0.1s
[CV 3/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.924, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.926, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.924, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.924, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.927, test=0.534) total time=   0.1s
[CV 8/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.923, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.917, test=0.567) total time=   0.1s
[CV 10/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.921, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.927, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.923, test=0.561) total time=   0.1s
[CV 3/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.924, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.926, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.924, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.924, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.927, test=0.534) total time=   0.1s
[CV 8/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.923, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.917, test=0.567) total time=   0.2s
[CV 10/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.921, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.927, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.922, test=0.561) total time=   0.1s
[CV 3/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.924, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.925, test=0.543) total time=   0.2s
[CV 5/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.924, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.924, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.926, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.923, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.917, test=0.567) total time=   0.1s
[CV 10/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.921, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.927, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.922, test=0.561) total time=   0.2s
[CV 3/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.924, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.925, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.924, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.924, test=0.542) total time=   0.2s
[CV 7/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.926, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.923, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.917, test=0.567) total time=   0.2s
[CV 10/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.921, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.927, test=0.561) total time=   0.2s
[CV 2/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.922, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.924, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.925, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.924, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 7/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.926, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.923, test=0.565) total time=   0.2s
[CV 9/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.917, test=0.567) total time=   0.2s
[CV 10/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.921, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.927, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.922, test=0.562) total time=   0.2s
[CV 3/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.924, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.925, test=0.543) total time=   0.2s
[CV 5/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.924, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.924, test=0.543) total time=   0.2s
[CV 7/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.926, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.923, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.917, test=0.566) total time=   0.1s
[CV 10/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.921, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.927, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.922, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.924, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.925, test=0.543) total time=   0.2s
[CV 5/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.924, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 7/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.926, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.923, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.917, test=0.566) total time=   0.1s
[CV 10/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.921, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.927, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.922, test=0.562) total time=   0.2s
[CV 3/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.924, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.924, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.924, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.926, test=0.534) total time=   0.2s
[CV 8/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.922, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.917, test=0.567) total time=   0.1s
[CV 10/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.921, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.925, test=0.562) total time=   0.2s
[CV 2/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.921, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.924, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.923, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.923, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.924, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.921, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.916, test=0.568) total time=   0.1s
[CV 10/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.920, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.925, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.922, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.916, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.919, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.925, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.922, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.916, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.919, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.925, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.922, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.916, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.919, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.925, test=0.561) total time=   0.2s
[CV 2/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.922, test=0.542) total time=   0.2s
[CV 7/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.564) total time=   0.2s
[CV 9/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.916, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.919, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.925, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.922, test=0.542) total time=   0.2s
[CV 7/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.916, test=0.569) total time=   0.2s
[CV 10/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.919, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.925, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.921, test=0.562) total time=   0.2s
[CV 3/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.923, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.923, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.922, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.924, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.921, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.916, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.919, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.925, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.921, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.923, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.923, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.922, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.924, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.921, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.916, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.919, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.925, test=0.561) total time=   0.2s
[CV 2/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.921, test=0.562) total time=   0.2s
[CV 3/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.923, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.923, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.923, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.922, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.924, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.921, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.915, test=0.568) total time=   0.1s
[CV 10/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.919, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.925, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.920, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.923, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.923, test=0.541) total time=   0.2s
[CV 5/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.923, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.922, test=0.541) total time=   0.2s
[CV 7/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.923, test=0.534) total time=   0.1s
[CV 8/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.920, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.915, test=0.568) total time=   0.1s
[CV 10/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.918, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.924, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.920, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.923, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.923, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.922, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.921, test=0.542) total time=   0.2s
[CV 7/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.923, test=0.534) total time=   0.1s
[CV 8/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.920, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.915, test=0.568) total time=   0.1s
[CV 10/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.918, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.924, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.923, test=0.542) total time=   0.2s
[CV 5/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.534) total time=   0.1s
[CV 8/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.914, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.918, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.924, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.923, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.534) total time=   0.2s
[CV 8/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.564) total time=   0.2s
[CV 9/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.914, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.918, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.924, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.923, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.542) total time=   0.2s
[CV 7/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.534) total time=   0.1s
[CV 8/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.914, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.918, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.924, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.923, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.534) total time=   0.1s
[CV 8/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.914, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.918, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.924, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.920, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.922, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.922, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.922, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.920, test=0.543) total time=   0.1s
[CV 7/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.922, test=0.534) total time=   0.1s
[CV 8/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.920, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.914, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.917, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.921, test=0.557) total time=   0.2s
[CV 2/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.917, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.919, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.920, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.919, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.917, test=0.546) total time=   0.1s
[CV 7/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.920, test=0.534) total time=   0.1s
[CV 8/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.918, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.912, test=0.572) total time=   0.1s
[CV 10/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.921, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.916, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.919, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.920, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.919, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.917, test=0.545) total time=   0.1s
[CV 7/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.919, test=0.536) total time=   0.1s
[CV 8/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.918, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.912, test=0.572) total time=   0.2s
[CV 10/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.920, test=0.557) total time=   0.2s
[CV 2/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.916, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.919, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.920, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.919, test=0.561) total time=   0.2s
[CV 6/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.916, test=0.546) total time=   0.2s
[CV 7/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.919, test=0.536) total time=   0.1s
[CV 8/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.918, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.912, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.920, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.916, test=0.562) total time=   0.2s
[CV 3/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.918, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.920, test=0.542) total time=   0.2s
[CV 5/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.919, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.915, test=0.546) total time=   0.2s
[CV 7/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.919, test=0.536) total time=   0.1s
[CV 8/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.918, test=0.564) total time=   0.2s
[CV 9/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.912, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.920, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.916, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.918, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.919, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.919, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 7/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.919, test=0.536) total time=   0.1s
[CV 8/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.917, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.912, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.920, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.916, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.918, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.919, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.918, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 7/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.919, test=0.536) total time=   0.1s
[CV 8/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.917, test=0.564) total time=   0.2s
[CV 9/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.912, test=0.572) total time=   0.1s
[CV 10/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.915, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.920, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.915, test=0.562) total time=   0.2s
[CV 3/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.917, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.919, test=0.541) total time=   0.1s
[CV 5/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.918, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 7/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.919, test=0.536) total time=   0.1s
[CV 8/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.917, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.911, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.913, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.919, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.915, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.917, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.919, test=0.541) total time=   0.1s
[CV 5/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.917, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 7/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.919, test=0.536) total time=   0.1s
[CV 8/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.911, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.912, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.919, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.915, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.917, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.917, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 7/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.919, test=0.536) total time=   0.1s
[CV 8/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.916, test=0.565) total time=   0.2s
[CV 9/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.911, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.912, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.919, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.915, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.917, test=0.555) total time=   0.2s
[CV 4/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.918, test=0.542) total time=   0.2s
[CV 5/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.917, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 7/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.919, test=0.536) total time=   0.2s
[CV 8/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.911, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.912, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.919, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.915, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.917, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.917, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.914, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.918, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.911, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.912, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.919, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.915, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.916, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.917, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.914, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.918, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.911, test=0.574) total time=   0.2s
[CV 10/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.912, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.919, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.915, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.916, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.917, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.913, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.917, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.911, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.912, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.919, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.915, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.916, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.918, test=0.542) total time=   0.2s
[CV 5/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.917, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.913, test=0.547) total time=   0.2s
[CV 7/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.917, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.916, test=0.565) total time=   0.2s
[CV 9/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.911, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.912, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.919, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.915, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.916, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.917, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.913, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.917, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.916, test=0.565) total time=   0.2s
[CV 9/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.911, test=0.574) total time=   0.2s
[CV 10/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.911, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.919, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.915, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.916, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.918, test=0.542) total time=   0.2s
[CV 5/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.917, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.913, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.917, test=0.537) total time=   0.2s
[CV 8/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.916, test=0.565) total time=   0.2s
[CV 9/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.911, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.911, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.919, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.915, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.916, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.916, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.913, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.917, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.911, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.910, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.918, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.914, test=0.563) total time=   0.2s
[CV 3/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.916, test=0.555) total time=   0.2s
[CV 4/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.916, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.913, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.917, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.916, test=0.565) total time=   0.2s
[CV 9/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.910, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.910, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.918, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.914, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.916, test=0.555) total time=   0.2s
[CV 4/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.916, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.913, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.917, test=0.537) total time=   0.2s
[CV 8/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.916, test=0.565) total time=   0.2s
[CV 9/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.910, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.910, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.917, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.914, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.915, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.918, test=0.542) total time=   0.2s
[CV 5/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.915, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.913, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.916, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.910, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.910, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.917, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.914, test=0.563) total time=   0.2s
[CV 3/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.915, test=0.555) total time=   0.2s
[CV 4/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.915, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.913, test=0.547) total time=   0.2s
[CV 7/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.916, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.910, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.910, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.917, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.914, test=0.563) total time=   0.2s
[CV 3/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.915, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.915, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.913, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.916, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.910, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.910, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.917, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.914, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.915, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.915, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.913, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.916, test=0.538) total time=   0.1s
[CV 8/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.910, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.910, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.917, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.914, test=0.563) total time=   0.2s
[CV 3/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.915, test=0.555) total time=   0.2s
[CV 4/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.918, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.915, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.913, test=0.548) total time=   0.1s
[CV 7/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.916, test=0.538) total time=   0.2s
[CV 8/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.915, test=0.566) total time=   0.1s
[CV 9/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.910, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.908, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.915, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.912, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.913, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.915, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.911, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.910, test=0.550) total time=   0.2s
[CV 7/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.914, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.914, test=0.568) total time=   0.2s
[CV 9/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.908, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.907, test=0.544) total time=   0.2s
[CV 1/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.914, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.912, test=0.564) total time=   0.2s
[CV 3/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.911, test=0.555) total time=   0.2s
[CV 6/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.909, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.568) total time=   0.1s
[CV 9/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.908, test=0.576) total time=   0.2s
[CV 10/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.906, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.914, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.912, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.911, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.909, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.539) total time=   0.2s
[CV 8/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.568) total time=   0.1s
[CV 9/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.908, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.906, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.914, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.912, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.911, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.909, test=0.551) total time=   0.2s
[CV 7/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.568) total time=   0.1s
[CV 9/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.908, test=0.576) total time=   0.2s
[CV 10/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.906, test=0.544) total time=   0.2s
[CV 1/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.914, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.912, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.911, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.909, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.568) total time=   0.1s
[CV 9/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.908, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.906, test=0.544) total time=   0.2s
[CV 1/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.914, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.912, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.911, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.909, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.568) total time=   0.1s
[CV 9/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.908, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.906, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.914, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.912, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.913, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.913, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.911, test=0.555) total time=   0.2s
[CV 6/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.909, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.913, test=0.538) total time=   0.1s
[CV 8/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.913, test=0.568) total time=   0.1s
[CV 9/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.908, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.906, test=0.544) total time=   0.2s
[CV 1/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.914, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.912, test=0.564) total time=   0.2s
[CV 3/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.913, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.913, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.911, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.909, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.913, test=0.538) total time=   0.1s
[CV 8/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.913, test=0.568) total time=   0.2s
[CV 9/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.908, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.906, test=0.544) total time=   0.2s
[CV 1/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.914, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.912, test=0.564) total time=   0.2s
[CV 3/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.913, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.913, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.911, test=0.555) total time=   0.2s
[CV 6/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.909, test=0.550) total time=   0.2s
[CV 7/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.913, test=0.538) total time=   0.2s
[CV 8/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.913, test=0.568) total time=   0.2s
[CV 9/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.908, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.906, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.914, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.912, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.913, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.913, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.911, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.909, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.913, test=0.538) total time=   0.2s
[CV 8/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.913, test=0.568) total time=   0.2s
[CV 9/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.908, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.906, test=0.543) total time=   0.2s
[CV 1/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.913, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.910, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.912, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.912, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.909, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.907, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.912, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.912, test=0.570) total time=   0.1s
[CV 9/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.907, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.905, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.912, test=0.561) total time=   0.2s
[CV 2/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.910, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.912, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.912, test=0.544) total time=   0.2s
[CV 5/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.909, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.906, test=0.552) total time=   0.2s
[CV 7/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.912, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.912, test=0.570) total time=   0.1s
[CV 9/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.905, test=0.578) total time=   0.2s
[CV 10/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.905, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.912, test=0.561) total time=   0.2s
[CV 2/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.910, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.912, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.912, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.909, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.906, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.911, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.911, test=0.571) total time=   0.1s
[CV 9/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.905, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.905, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.912, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.909, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.912, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.912, test=0.544) total time=   0.2s
[CV 5/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.908, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.906, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.911, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.911, test=0.571) total time=   0.2s
[CV 9/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.905, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.905, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.912, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.909, test=0.563) total time=   0.2s
[CV 3/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.911, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.912, test=0.544) total time=   0.2s
[CV 5/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.908, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.906, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.911, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.911, test=0.571) total time=   0.1s
[CV 9/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.905, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.904, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.912, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.909, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.911, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.912, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.908, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.906, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.911, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.911, test=0.571) total time=   0.1s
[CV 9/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.905, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.904, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.912, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.909, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.911, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.912, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.907, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.906, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.911, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.911, test=0.571) total time=   0.1s
[CV 9/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.905, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.903, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.912, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.909, test=0.563) total time=   0.2s
[CV 3/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.911, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.911, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.907, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.906, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.910, test=0.538) total time=   0.1s
[CV 8/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.910, test=0.570) total time=   0.1s
[CV 9/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.905, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.903, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.912, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.909, test=0.563) total time=   0.2s
[CV 3/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.911, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.911, test=0.543) total time=   0.2s
[CV 5/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.907, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.906, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.910, test=0.538) total time=   0.1s
[CV 8/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.910, test=0.570) total time=   0.1s
[CV 9/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.905, test=0.577) total time=   0.2s
[CV 10/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.903, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.912, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.909, test=0.563) total time=   0.2s
[CV 3/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.911, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.911, test=0.543) total time=   0.2s
[CV 5/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.907, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.906, test=0.552) total time=   0.2s
[CV 7/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.910, test=0.538) total time=   0.1s
[CV 8/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.910, test=0.570) total time=   0.2s
[CV 9/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.905, test=0.577) total time=   0.2s
[CV 10/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.903, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.911, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.909, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.911, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.911, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.907, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.905, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.910, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.910, test=0.571) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.905, test=0.577) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.903, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.911, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.909, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.911, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.911, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.907, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.905, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.910, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.910, test=0.571) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.905, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.903, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.910, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.907, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.910, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.910, test=0.543) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.906, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.904, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.908, test=0.540) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.909, test=0.571) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.904, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.902, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.909, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.907, test=0.564) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.910, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.910, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.906, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.904, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.908, test=0.540) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.909, test=0.571) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.904, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.901, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.909, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.907, test=0.564) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.910, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.910, test=0.543) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.906, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.904, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.908, test=0.540) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.909, test=0.571) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.904, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.901, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.909, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.906, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.910, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.910, test=0.543) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.905, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.904, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.908, test=0.540) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.909, test=0.571) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.904, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.901, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.908, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.905, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.908, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.909, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.905, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.903, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.907, test=0.540) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.907, test=0.569) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.903, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.900, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.907, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.905, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.908, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.909, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.905, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.903, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.907, test=0.540) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.907, test=0.569) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.903, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.900, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.907, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.905, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.907, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.909, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.905, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.903, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.907, test=0.540) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.906, test=0.567) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.902, test=0.576) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.899, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.907, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.905, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.907, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.909, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.904, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.903, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.907, test=0.540) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.906, test=0.567) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.902, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.899, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.907, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.905, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.907, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.909, test=0.544) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.904, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.903, test=0.555) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.907, test=0.540) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.905, test=0.568) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.901, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.899, test=0.544) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.907, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.905, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.907, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.909, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.904, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.903, test=0.555) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.907, test=0.540) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.905, test=0.568) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.901, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.899, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.906, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.905, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.907, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.908, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.903, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.902, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.907, test=0.540) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.905, test=0.568) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.901, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.898, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.905, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.904, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.907, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.907, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.902, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.901, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.906, test=0.541) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.904, test=0.571) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.900, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.897, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.905, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.904, test=0.567) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.907, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.907, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.900, test=0.554) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.905, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.903, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.900, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.897, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.000101328320802005;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000101328320802005;, score=(train=0.903, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000101328320802005;, score=(train=0.906, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.000101328320802005;, score=(train=0.906, test=0.545) total time=   0.2s
[CV 5/10] END ccp_alpha=0.000101328320802005;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000101328320802005;, score=(train=0.900, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000101328320802005;, score=(train=0.905, test=0.543) total time=   0.2s
[CV 8/10] END ccp_alpha=0.000101328320802005;, score=(train=0.903, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.000101328320802005;, score=(train=0.900, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000101328320802005;, score=(train=0.896, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.903, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.906, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.906, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.900, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.905, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.903, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.900, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.896, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.903, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.906, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.906, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.902, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.900, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.905, test=0.543) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.903, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.900, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.896, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.905, test=0.562) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.903, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.906, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.906, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.900, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.905, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.903, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.900, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.896, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.903, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.905, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.906, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.899, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.905, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.903, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.899, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.896, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.903, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.905, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.906, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.899, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.905, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.903, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.899, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.896, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.903, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.905, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.906, test=0.544) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.899, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.905, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.902, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.899, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.896, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.902, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.905, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.906, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.899, test=0.552) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.905, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.902, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.899, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.895, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.902, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.905, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.906, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.899, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.905, test=0.543) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.902, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.899, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.895, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.902, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.905, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.906, test=0.544) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.899, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.905, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.902, test=0.572) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.899, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.895, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.902, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.905, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.906, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.899, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.904, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.902, test=0.571) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.899, test=0.580) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.895, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.904, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.901, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.903, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.904, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.898, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.903, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.902, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.897, test=0.580) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.894, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.903, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.901, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.903, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.904, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.898, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.903, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.901, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.897, test=0.580) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.894, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.903, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.901, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.903, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.904, test=0.545) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.898, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.903, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.901, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.897, test=0.580) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.894, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.903, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.901, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.903, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.904, test=0.545) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.898, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.903, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.901, test=0.572) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.897, test=0.580) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.894, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.901, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.900, test=0.565) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.903, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.904, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.900, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.898, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.903, test=0.542) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.901, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.897, test=0.580) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.893, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.901, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.899, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.903, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.898, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.897, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.902, test=0.544) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.900, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.896, test=0.580) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.893, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.901, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.899, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.903, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.898, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.897, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.902, test=0.544) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.900, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.896, test=0.580) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.893, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.901, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.899, test=0.565) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.903, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.898, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.897, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.902, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.900, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.896, test=0.580) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.893, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.901, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.899, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.902, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.898, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.897, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.902, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.900, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.896, test=0.580) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.893, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.900, test=0.563) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.898, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.902, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.898, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.897, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.902, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.900, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.895, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.893, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.900, test=0.563) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.898, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.902, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.898, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.897, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.902, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.900, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.895, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.893, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.900, test=0.563) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.898, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.902, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.898, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.897, test=0.550) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.902, test=0.545) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.900, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.895, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.893, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.900, test=0.563) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.898, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.901, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.902, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.897, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.896, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.901, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.900, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.894, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.893, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.899, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.897, test=0.565) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.900, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.902, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.897, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.896, test=0.550) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.901, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.900, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.894, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.892, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.899, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.897, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.899, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.902, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.897, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.895, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.900, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.900, test=0.575) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.894, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.892, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.899, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.897, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.899, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.902, test=0.545) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.897, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.895, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.900, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.900, test=0.575) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.894, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.892, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.899, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.897, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.899, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.902, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.897, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.895, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.900, test=0.545) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.900, test=0.575) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.894, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.892, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.899, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.896, test=0.567) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.899, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.901, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.896, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.895, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.899, test=0.546) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.899, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.893, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.890, test=0.549) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.898, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.896, test=0.567) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.899, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.901, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.896, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.895, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.899, test=0.546) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.899, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.893, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.889, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.898, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.896, test=0.567) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.899, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.901, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.895, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.895, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.899, test=0.547) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.899, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.893, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.889, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.898, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.896, test=0.567) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.899, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.901, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.895, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.895, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.899, test=0.547) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.899, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.893, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.889, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.898, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.896, test=0.567) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.899, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.901, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.895, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.895, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.899, test=0.546) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.898, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.893, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.889, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.898, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.896, test=0.567) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.899, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.901, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.895, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.895, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.899, test=0.547) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.898, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.893, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.889, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.898, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.896, test=0.567) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.899, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.901, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.895, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.895, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.899, test=0.547) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.898, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.893, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.889, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.897, test=0.563) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.895, test=0.569) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.897, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.900, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.894, test=0.561) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.894, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.898, test=0.546) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.898, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.892, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.888, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.897, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.895, test=0.569) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.897, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.899, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.894, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.894, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.895, test=0.546) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.898, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.892, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.888, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.897, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.894, test=0.569) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.896, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.899, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.893, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.894, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.894, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.897, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.891, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.887, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.897, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.894, test=0.569) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.896, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.899, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.892, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.894, test=0.553) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.894, test=0.545) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.897, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.890, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.887, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.897, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.893, test=0.570) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.896, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.899, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.892, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.894, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.894, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.897, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.890, test=0.578) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.887, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.897, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.893, test=0.570) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.896, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.899, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.891, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.894, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.894, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.897, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.890, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.887, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.896, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.893, test=0.570) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.895, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.899, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.891, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.893, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.893, test=0.547) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.896, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.889, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.886, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.896, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.893, test=0.570) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.895, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.899, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.891, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.893, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.893, test=0.547) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.896, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.889, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.886, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.896, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.893, test=0.570) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.895, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.899, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.891, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.893, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.893, test=0.547) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.896, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.889, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.886, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.896, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.893, test=0.571) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.894, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.899, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.891, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.893, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.893, test=0.547) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.896, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.889, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.886, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.896, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.893, test=0.571) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.894, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.899, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.890, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.892, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.893, test=0.546) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.896, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.889, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.896, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.893, test=0.571) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.894, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.898, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.890, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.892, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.893, test=0.546) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.896, test=0.575) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.889, test=0.577) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.884, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.896, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.893, test=0.571) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.894, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.898, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.890, test=0.561) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.891, test=0.554) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.892, test=0.546) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.896, test=0.575) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.889, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.884, test=0.549) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.896, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.893, test=0.571) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.894, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.898, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.890, test=0.561) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.891, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.892, test=0.546) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.896, test=0.575) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.888, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.884, test=0.549) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.896, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.893, test=0.571) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.894, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.898, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.890, test=0.561) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.890, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.892, test=0.546) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.895, test=0.575) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.887, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.883, test=0.549) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.896, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.893, test=0.571) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.894, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.897, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.889, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.890, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.892, test=0.546) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.895, test=0.575) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.887, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.883, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.896, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.893, test=0.571) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.894, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.897, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.889, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.890, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.891, test=0.546) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.895, test=0.575) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.887, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.883, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.895, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.893, test=0.571) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.894, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.897, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.889, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.890, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.891, test=0.546) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.894, test=0.574) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.887, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.882, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.000105;, score=(train=0.895, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.000105;, score=(train=0.893, test=0.571) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000105;, score=(train=0.894, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.000105;, score=(train=0.897, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.000105;, score=(train=0.889, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=0.000105;, score=(train=0.890, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000105;, score=(train=0.891, test=0.546) total time=   0.2s
[CV 8/10] END ccp_alpha=0.000105;, score=(train=0.894, test=0.574) total time=   0.2s
[CV 9/10] END ccp_alpha=0.000105;, score=(train=0.887, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000105;, score=(train=0.882, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000105;, score=(train=0.895, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000105;, score=(train=0.893, test=0.571) total time=   0.2s
[CV 3/10] END ccp_alpha=0.000105;, score=(train=0.894, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.000105;, score=(train=0.897, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.000105;, score=(train=0.889, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000105;, score=(train=0.890, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000105;, score=(train=0.891, test=0.546) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000105;, score=(train=0.894, test=0.574) total time=   0.1s
[CV 9/10] END ccp_alpha=0.000105;, score=(train=0.887, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000105;, score=(train=0.882, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000105;, score=(train=0.895, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000105;, score=(train=0.893, test=0.571) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000105;, score=(train=0.894, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.000105;, score=(train=0.897, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.000105;, score=(train=0.889, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=0.000105;, score=(train=0.890, test=0.555) total time=   0.2s
[CV 7/10] END ccp_alpha=0.000105;, score=(train=0.891, test=0.546) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000105;, score=(train=0.894, test=0.574) total time=   0.1s
[CV 9/10] END ccp_alpha=0.000105;, score=(train=0.887, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000105;, score=(train=0.882, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.895, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.892, test=0.572) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.893, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.897, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.889, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.890, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.890, test=0.549) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.893, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.887, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.882, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.895, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.892, test=0.572) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.893, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.897, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.889, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.890, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.890, test=0.549) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.893, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.887, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.882, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.895, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.891, test=0.573) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.893, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.895, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.888, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.890, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.888, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.893, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.887, test=0.575) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.882, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.895, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.891, test=0.573) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.893, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.895, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.888, test=0.563) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.890, test=0.555) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.888, test=0.551) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.893, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.887, test=0.575) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.882, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.895, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.891, test=0.574) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.893, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.895, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.888, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.889, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.888, test=0.551) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.893, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.885, test=0.572) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.882, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.895, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.891, test=0.574) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.893, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.895, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.888, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.889, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.888, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.893, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.885, test=0.572) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.882, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.895, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.890, test=0.575) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.892, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.895, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.888, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.889, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.888, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.893, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.885, test=0.572) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.882, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.895, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.890, test=0.575) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.892, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.895, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.888, test=0.563) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.889, test=0.557) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.888, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.893, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.885, test=0.572) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.882, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.895, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.890, test=0.575) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.892, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.895, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.888, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.889, test=0.557) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.888, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.893, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.885, test=0.572) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.881, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.895, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.890, test=0.575) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.892, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.895, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.888, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.889, test=0.556) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.888, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.891, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.884, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.881, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.894, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.889, test=0.574) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.892, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.895, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.888, test=0.563) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.889, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.888, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.891, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.884, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.881, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.894, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.889, test=0.574) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.891, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.895, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.888, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.886, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.888, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.890, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.883, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.880, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.893, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.889, test=0.574) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.889, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.894, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.887, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.886, test=0.556) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.887, test=0.550) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.890, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.881, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.879, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.888, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.889, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.887, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.884, test=0.556) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.886, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.890, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.881, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.892, test=0.565) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.888, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.889, test=0.561) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.887, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.886, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.890, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.881, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.879, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.887, test=0.575) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.886, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.884, test=0.555) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.890, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.887, test=0.575) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.893, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.886, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.890, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.887, test=0.575) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.886, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.890, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.892, test=0.565) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.887, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.888, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.893, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.886, test=0.563) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.889, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.880, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.887, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.886, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.884, test=0.556) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.889, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.887, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.886, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.885, test=0.551) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.889, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.879, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.887, test=0.576) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.886, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.889, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.880, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.886, test=0.576) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.888, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.886, test=0.563) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.885, test=0.551) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.889, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.880, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.886, test=0.576) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.886, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.889, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.880, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.886, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.886, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.889, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.892, test=0.565) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.886, test=0.576) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.885, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.884, test=0.556) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.889, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.886, test=0.576) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.885, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.884, test=0.556) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.889, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.886, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.888, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.885, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.884, test=0.556) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.885, test=0.551) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.889, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.891, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.886, test=0.576) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.892, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.885, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.885, test=0.552) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.888, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.879, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.891, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.886, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.888, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.892, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.885, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.884, test=0.556) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.885, test=0.552) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.888, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.890, test=0.566) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.886, test=0.575) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.891, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.885, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.883, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.884, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.888, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.879, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.877, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.889, test=0.567) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.886, test=0.575) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.887, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.891, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.885, test=0.563) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.883, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.884, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.887, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.879, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.877, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.889, test=0.567) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.885, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.887, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.891, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.884, test=0.562) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.883, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.884, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.887, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.879, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.877, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.889, test=0.567) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.885, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.887, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.891, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.884, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.883, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.884, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.886, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.879, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.876, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.889, test=0.567) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.883, test=0.577) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.886, test=0.559) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.890, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.884, test=0.561) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.883, test=0.555) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.884, test=0.553) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.886, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.878, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.875, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.889, test=0.567) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.883, test=0.577) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.886, test=0.559) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.890, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.884, test=0.561) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.883, test=0.555) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.884, test=0.553) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.886, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.878, test=0.574) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.875, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.888, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.881, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.885, test=0.559) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.889, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.883, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.883, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.883, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.886, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.878, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.875, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.888, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.880, test=0.579) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.885, test=0.559) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.889, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.883, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.883, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.883, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.886, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.878, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.875, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.888, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.880, test=0.579) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.885, test=0.559) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.889, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.883, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.882, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.881, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.886, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.877, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.874, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.888, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.880, test=0.579) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.885, test=0.559) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.889, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.883, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.882, test=0.554) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.880, test=0.553) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.886, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.877, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.874, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.888, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.880, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.885, test=0.559) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.889, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.883, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.882, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.880, test=0.553) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.886, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.877, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.874, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.888, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.880, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.884, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.889, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.883, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.882, test=0.554) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.880, test=0.553) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.877, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.874, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.888, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.880, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.884, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.889, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.883, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.882, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.880, test=0.553) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.877, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.874, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.887, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.880, test=0.579) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.883, test=0.559) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.888, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.883, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.882, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.880, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.885, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.876, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.874, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.887, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.880, test=0.580) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.883, test=0.559) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.888, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.883, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.882, test=0.554) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.880, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.876, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.874, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.886, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.880, test=0.580) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.883, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.888, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.883, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.881, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.880, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.876, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.873, test=0.543) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.886, test=0.565) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.879, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.883, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.886, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.882, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.881, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.878, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.876, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.873, test=0.543) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.886, test=0.565) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.879, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.883, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.886, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.882, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.881, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.878, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.885, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.876, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.873, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.886, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.879, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.883, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.886, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.882, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.881, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.878, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.876, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.873, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.886, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.879, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.883, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.886, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.882, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.881, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.878, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.885, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.876, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.873, test=0.543) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.886, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.879, test=0.579) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.882, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.886, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.882, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.881, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.878, test=0.554) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.885, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.876, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.873, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.886, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.879, test=0.579) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.882, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.885, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.882, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.881, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.878, test=0.555) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.876, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.873, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.886, test=0.565) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.878, test=0.580) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.882, test=0.561) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.884, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.881, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.881, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.878, test=0.555) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.875, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.873, test=0.544) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.886, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.878, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.882, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.884, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.881, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.881, test=0.552) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.878, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.875, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.873, test=0.544) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.885, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.878, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.882, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.884, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.881, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.881, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.877, test=0.555) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.875, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.873, test=0.543) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.885, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.877, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.882, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.884, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.881, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.881, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.877, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.884, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.875, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.873, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.885, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.877, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.882, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.884, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.881, test=0.563) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.881, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.877, test=0.554) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.884, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.875, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.873, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.884, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.875, test=0.581) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.879, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.884, test=0.545) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.880, test=0.565) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.880, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.876, test=0.555) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.883, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.874, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.873, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.884, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.875, test=0.581) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.879, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.884, test=0.545) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.880, test=0.565) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.880, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.876, test=0.555) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.883, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.874, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.873, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.884, test=0.562) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.875, test=0.581) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.879, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.883, test=0.545) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.878, test=0.565) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.880, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.876, test=0.555) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.883, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.873, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.871, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.883, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.875, test=0.581) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.879, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.883, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.878, test=0.566) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.880, test=0.552) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.876, test=0.555) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.883, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.873, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.871, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.882, test=0.566) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.875, test=0.580) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.878, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.882, test=0.544) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.877, test=0.568) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.878, test=0.552) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.874, test=0.558) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.883, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.872, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.871, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.882, test=0.566) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.875, test=0.580) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.878, test=0.562) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.882, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.877, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.878, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.874, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.883, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.872, test=0.574) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.871, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.882, test=0.566) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.875, test=0.580) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.878, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.882, test=0.544) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.877, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.878, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.874, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.883, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.872, test=0.574) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.871, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.882, test=0.566) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.875, test=0.580) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.878, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.882, test=0.544) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.877, test=0.568) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.878, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.874, test=0.558) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.883, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.872, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.871, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.882, test=0.566) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.875, test=0.580) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.878, test=0.562) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.882, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.877, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.878, test=0.551) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.874, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.882, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.872, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.871, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.882, test=0.566) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.875, test=0.580) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.878, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.881, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.877, test=0.568) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.878, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.874, test=0.559) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.882, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.872, test=0.574) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.870, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.880, test=0.568) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.873, test=0.582) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.877, test=0.562) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.880, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.875, test=0.567) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.877, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.872, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.880, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.871, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.870, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.880, test=0.568) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.873, test=0.582) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.877, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.880, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.875, test=0.567) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.877, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.872, test=0.559) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.880, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.871, test=0.574) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.870, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.880, test=0.568) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.872, test=0.581) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.877, test=0.562) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.880, test=0.545) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.875, test=0.567) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.877, test=0.552) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.872, test=0.559) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.879, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.871, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.869, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.880, test=0.568) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.872, test=0.581) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.877, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.874, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.876, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.872, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.879, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.870, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.868, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.880, test=0.568) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.872, test=0.581) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.876, test=0.563) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.874, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.876, test=0.551) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.872, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.879, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.870, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.868, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.880, test=0.568) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.872, test=0.581) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.876, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.874, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.876, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.872, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.879, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.870, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.868, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.880, test=0.568) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.871, test=0.580) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.876, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.874, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.876, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.878, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.870, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.868, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.880, test=0.568) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.871, test=0.580) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.876, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.878, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.874, test=0.568) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.876, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.871, test=0.560) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.878, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.870, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.868, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.880, test=0.568) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.871, test=0.580) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.876, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.874, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.875, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.871, test=0.560) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.878, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.870, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.868, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.880, test=0.568) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.871, test=0.580) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.875, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.874, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.875, test=0.552) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.878, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.870, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.868, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.879, test=0.568) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.871, test=0.580) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.875, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.874, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.875, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.878, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.870, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.868, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.879, test=0.568) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.871, test=0.580) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.875, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.874, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.875, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.878, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.870, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.868, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.879, test=0.568) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.871, test=0.580) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.875, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.873, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.875, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.878, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.870, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.868, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.879, test=0.568) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.871, test=0.581) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.875, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.873, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.874, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.876, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.870, test=0.576) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.867, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.879, test=0.568) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.871, test=0.581) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.875, test=0.562) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.873, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.874, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.876, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.870, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.867, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.879, test=0.569) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.870, test=0.582) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.875, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.873, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.873, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.876, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.868, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.867, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.879, test=0.569) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.870, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.875, test=0.561) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.873, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.873, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.876, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.868, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.866, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.878, test=0.569) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.870, test=0.583) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.875, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.877, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.873, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.872, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.876, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.868, test=0.578) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.866, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.878, test=0.569) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.870, test=0.583) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.875, test=0.561) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.877, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.872, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.872, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.871, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.875, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.868, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.866, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.878, test=0.569) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.870, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.875, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.877, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.872, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.872, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.870, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.874, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.868, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.866, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.876, test=0.569) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.869, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.874, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.876, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.872, test=0.569) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.872, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.869, test=0.560) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.873, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.868, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.866, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.876, test=0.569) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.869, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.874, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.876, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.872, test=0.569) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.872, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.869, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.868, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.866, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.874, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.868, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.873, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.875, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.872, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.871, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.869, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.868, test=0.580) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.865, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001125;, score=(train=0.874, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001125;, score=(train=0.868, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001125;, score=(train=0.873, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001125;, score=(train=0.875, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001125;, score=(train=0.871, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001125;, score=(train=0.871, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001125;, score=(train=0.869, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001125;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001125;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001125;, score=(train=0.865, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001125;, score=(train=0.874, test=0.570) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001125;, score=(train=0.868, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001125;, score=(train=0.873, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001125;, score=(train=0.875, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001125;, score=(train=0.871, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001125;, score=(train=0.871, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001125;, score=(train=0.869, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001125;, score=(train=0.873, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001125;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001125;, score=(train=0.865, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.874, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.868, test=0.583) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.873, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.875, test=0.547) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.871, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.871, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.869, test=0.559) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.865, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.874, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.868, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.873, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.875, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.871, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.871, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.869, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.873, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.865, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.874, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.868, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.873, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.875, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.871, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.871, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.869, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.865, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.874, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.868, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.873, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.875, test=0.547) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.871, test=0.569) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.871, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.869, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.873, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.865, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.874, test=0.570) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.868, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.873, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.874, test=0.546) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.871, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.871, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.869, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.873, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.866, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.865, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.874, test=0.570) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.868, test=0.583) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.873, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.874, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.871, test=0.569) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.871, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.869, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.866, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.865, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.874, test=0.570) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.868, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.873, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.874, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.871, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.871, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.869, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.866, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.865, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.874, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.867, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.873, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.874, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.871, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.871, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.869, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.866, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.865, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.873, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.865, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.872, test=0.561) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.874, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.871, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.870, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.868, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.873, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.865, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.873, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.865, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.872, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.874, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.871, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.870, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.868, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.864, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.872, test=0.571) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.865, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.871, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.874, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.871, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.870, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.867, test=0.558) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.873, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.866, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.864, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.872, test=0.571) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.865, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.871, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.874, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.871, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.870, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.867, test=0.558) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.864, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.872, test=0.570) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.865, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.872, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.871, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.870, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.867, test=0.557) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.866, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.864, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.872, test=0.570) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.863, test=0.586) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.872, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.871, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.870, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.867, test=0.557) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.872, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.865, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.864, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.872, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.863, test=0.586) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.872, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.871, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.870, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.867, test=0.557) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.872, test=0.579) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.865, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.864, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.872, test=0.571) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.863, test=0.586) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.871, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.871, test=0.568) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.870, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.866, test=0.557) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.872, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.865, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.864, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.872, test=0.571) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.863, test=0.586) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.871, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.871, test=0.568) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.870, test=0.554) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.866, test=0.557) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.872, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.865, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.864, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.871, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.863, test=0.586) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.871, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.871, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.871, test=0.568) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.870, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.866, test=0.557) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.872, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.865, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.864, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.871, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.863, test=0.586) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.871, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.871, test=0.568) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.870, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.866, test=0.557) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.872, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.865, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.864, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.870, test=0.572) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.862, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.870, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.871, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.870, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.869, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.866, test=0.557) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.872, test=0.580) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.865, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.863, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.870, test=0.572) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.862, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.870, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.871, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.870, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.869, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.866, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.872, test=0.580) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.865, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.863, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.870, test=0.572) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.861, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.870, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.871, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.870, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.869, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.866, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.872, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.865, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.862, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.870, test=0.572) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.859, test=0.586) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.869, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.870, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.869, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.869, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.866, test=0.558) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.872, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.863, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.862, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.868, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.858, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.868, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.870, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.868, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.868, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.865, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.870, test=0.580) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.863, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.861, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.868, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.857, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.868, test=0.562) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.870, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.867, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.867, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.865, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.870, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.863, test=0.582) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.859, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.868, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.857, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.868, test=0.562) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.870, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.867, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.866, test=0.552) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.865, test=0.559) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.870, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.863, test=0.582) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.859, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.868, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.857, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.868, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.870, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.867, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.866, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.865, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.869, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.863, test=0.582) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.859, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.868, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.857, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.868, test=0.562) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.870, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.867, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.866, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.865, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.869, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.863, test=0.582) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.859, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.867, test=0.571) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.856, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.867, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.869, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.866, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.866, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.865, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.868, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.862, test=0.583) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.856, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.867, test=0.571) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.856, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.867, test=0.563) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.868, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.866, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.866, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.864, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.868, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.862, test=0.583) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.856, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.867, test=0.571) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.856, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.867, test=0.563) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.868, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.865, test=0.570) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.866, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.864, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.868, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.861, test=0.583) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.856, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.867, test=0.571) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.856, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.866, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.868, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.864, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.866, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.864, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.868, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.860, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.856, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.863, test=0.569) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.855, test=0.586) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.866, test=0.563) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.863, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.863, test=0.570) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.864, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.863, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.868, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.859, test=0.585) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.855, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.863, test=0.569) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.855, test=0.586) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.866, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.863, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.862, test=0.570) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.864, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.863, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.868, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.859, test=0.585) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.855, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.863, test=0.569) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.855, test=0.586) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.866, test=0.562) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.863, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.861, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.864, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.863, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.868, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.858, test=0.584) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.855, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.863, test=0.569) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.855, test=0.586) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.866, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.862, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.860, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.863, test=0.554) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.861, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.868, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.858, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.855, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.863, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.855, test=0.586) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.865, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.860, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.858, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.861, test=0.556) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.860, test=0.562) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.868, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.858, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.854, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.863, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.855, test=0.586) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.865, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.860, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.858, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.861, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.860, test=0.562) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.868, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.858, test=0.584) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.854, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.863, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.855, test=0.586) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.865, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.860, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.858, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.861, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.860, test=0.562) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.868, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.858, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.854, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.862, test=0.571) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.854, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.865, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.860, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.858, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.860, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.859, test=0.560) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.857, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.854, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.862, test=0.571) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.854, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.865, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.860, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.858, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.859, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.859, test=0.560) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.857, test=0.584) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.853, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.862, test=0.571) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.854, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.865, test=0.563) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.860, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.858, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.859, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.859, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.855, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.853, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.862, test=0.571) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.854, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.865, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.860, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.858, test=0.569) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.859, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.859, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.855, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.853, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.862, test=0.571) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.854, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.865, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.860, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.858, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.859, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.858, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.855, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.853, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.861, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.854, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.865, test=0.563) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.860, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.858, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.859, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.858, test=0.561) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.866, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.855, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.853, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.861, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.854, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.865, test=0.563) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.860, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.857, test=0.569) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.859, test=0.555) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.858, test=0.562) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.855, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.853, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.861, test=0.570) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.854, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.865, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.860, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.857, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.859, test=0.555) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.858, test=0.562) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.855, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.853, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.861, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.854, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.864, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.859, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.857, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.858, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.857, test=0.561) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.866, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.855, test=0.585) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.853, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.861, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.853, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.864, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.859, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.855, test=0.571) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.857, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.857, test=0.562) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.865, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.854, test=0.585) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.853, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.859, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.852, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.863, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.858, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.854, test=0.572) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.855, test=0.558) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.855, test=0.563) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.863, test=0.580) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.850, test=0.585) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.851, test=0.551) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.859, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.852, test=0.583) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.863, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.857, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.854, test=0.572) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.855, test=0.558) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.855, test=0.563) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.863, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.850, test=0.585) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.851, test=0.551) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.859, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.851, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.863, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.857, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.854, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.855, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.855, test=0.563) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.863, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.850, test=0.586) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.851, test=0.551) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.859, test=0.573) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.851, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.863, test=0.563) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.857, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.854, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.855, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.855, test=0.563) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.863, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.850, test=0.586) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.851, test=0.551) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.859, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.851, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.863, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.857, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.854, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.855, test=0.557) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.855, test=0.563) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.863, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.850, test=0.586) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.851, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.859, test=0.573) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.851, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.863, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.857, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.854, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.855, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.855, test=0.563) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.863, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.849, test=0.586) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.851, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.859, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.851, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.861, test=0.565) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.857, test=0.552) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.854, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.854, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.855, test=0.563) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.861, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.849, test=0.586) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.851, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.859, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.851, test=0.584) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.860, test=0.566) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.855, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.854, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.854, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.855, test=0.563) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.861, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.849, test=0.586) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.850, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.859, test=0.573) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.850, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.860, test=0.566) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.855, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.854, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.854, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.855, test=0.563) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.861, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.849, test=0.586) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.850, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.858, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.850, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.860, test=0.566) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.855, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.854, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.854, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.855, test=0.563) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.861, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.849, test=0.586) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.849, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.858, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.850, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.860, test=0.566) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.854, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.854, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.854, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.855, test=0.563) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.861, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.849, test=0.586) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.849, test=0.551) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.857, test=0.575) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.848, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.858, test=0.567) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.854, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.852, test=0.573) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.853, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.853, test=0.564) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.859, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.849, test=0.585) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.848, test=0.551) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.857, test=0.575) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.848, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.858, test=0.567) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.854, test=0.553) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.852, test=0.573) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.853, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.853, test=0.564) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.859, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.849, test=0.585) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.848, test=0.551) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.856, test=0.576) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.848, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.858, test=0.566) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.853, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.852, test=0.573) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.852, test=0.559) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.853, test=0.564) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.859, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.848, test=0.586) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.848, test=0.551) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.856, test=0.576) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.848, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.857, test=0.566) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.853, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.852, test=0.573) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.851, test=0.561) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.852, test=0.564) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.859, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.847, test=0.587) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.847, test=0.552) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.854, test=0.575) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.847, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.856, test=0.566) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.852, test=0.552) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.851, test=0.572) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.849, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.851, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.858, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.845, test=0.589) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.846, test=0.552) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.852, test=0.576) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.847, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.856, test=0.566) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.852, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.851, test=0.572) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.849, test=0.562) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.850, test=0.566) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.857, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.845, test=0.589) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.846, test=0.551) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.851, test=0.576) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.845, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.854, test=0.567) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.850, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.850, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.848, test=0.562) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.849, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.855, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.844, test=0.589) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.845, test=0.551) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.851, test=0.576) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.845, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.854, test=0.567) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.850, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.850, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.848, test=0.562) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.849, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.855, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.844, test=0.589) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.845, test=0.551) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.851, test=0.576) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.845, test=0.582) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.853, test=0.568) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.850, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.850, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.848, test=0.562) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.849, test=0.565) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.855, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.844, test=0.589) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.845, test=0.551) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.850, test=0.576) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.845, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.853, test=0.568) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.850, test=0.552) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.850, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.848, test=0.562) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.849, test=0.565) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.855, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.844, test=0.589) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.845, test=0.552) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.850, test=0.576) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.845, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.853, test=0.568) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.850, test=0.552) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.850, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.848, test=0.562) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.849, test=0.565) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.855, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.844, test=0.589) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.845, test=0.552) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.849, test=0.574) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.845, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.851, test=0.567) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.848, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.849, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.847, test=0.562) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.845, test=0.564) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.854, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.842, test=0.590) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.842, test=0.552) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.846, test=0.575) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.843, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.851, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.848, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.849, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.847, test=0.562) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.844, test=0.564) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.854, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.841, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.842, test=0.553) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.846, test=0.575) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.843, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.851, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.847, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.849, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.847, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.844, test=0.564) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.854, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.841, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.842, test=0.553) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.846, test=0.575) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.843, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.849, test=0.570) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.847, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.848, test=0.573) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.847, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.844, test=0.564) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.854, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.841, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.842, test=0.555) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.845, test=0.576) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.843, test=0.582) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.846, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.847, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.848, test=0.573) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.844, test=0.559) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.843, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.852, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.840, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.840, test=0.556) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.845, test=0.576) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.843, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.846, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.847, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.848, test=0.573) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.844, test=0.559) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.843, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.852, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.840, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.840, test=0.556) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.845, test=0.576) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.843, test=0.582) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.846, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.847, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.848, test=0.573) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.844, test=0.559) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.843, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.852, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.840, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.840, test=0.556) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.845, test=0.576) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.843, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.846, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.847, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.848, test=0.573) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.844, test=0.559) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.843, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.852, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.840, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.840, test=0.556) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.844, test=0.577) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.843, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.846, test=0.568) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.847, test=0.552) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.848, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.842, test=0.558) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.843, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.852, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.840, test=0.590) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.840, test=0.555) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.844, test=0.577) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.842, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.845, test=0.568) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.847, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.847, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.842, test=0.558) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.843, test=0.565) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.852, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.839, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.836, test=0.554) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.842, test=0.578) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.842, test=0.582) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.845, test=0.568) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.847, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.847, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.842, test=0.558) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.843, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.852, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.839, test=0.591) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.836, test=0.554) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.841, test=0.578) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.842, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.844, test=0.568) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.845, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.846, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.842, test=0.557) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.842, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.849, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.837, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.836, test=0.554) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.841, test=0.578) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.841, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.844, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.845, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.845, test=0.575) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.842, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.841, test=0.566) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.849, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.837, test=0.592) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.835, test=0.555) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.841, test=0.578) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.841, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.844, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.845, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.845, test=0.575) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.842, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.841, test=0.566) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.849, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.837, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.835, test=0.555) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.841, test=0.578) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.841, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.844, test=0.569) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.845, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.844, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.841, test=0.558) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.841, test=0.566) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.848, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.837, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.833, test=0.555) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.841, test=0.578) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.841, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.844, test=0.569) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.845, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.843, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.841, test=0.558) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.841, test=0.566) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.848, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.837, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.833, test=0.555) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.841, test=0.578) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.841, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.844, test=0.569) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.845, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.843, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.841, test=0.558) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.841, test=0.566) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.848, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.837, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.833, test=0.555) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.840, test=0.580) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.840, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.843, test=0.567) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.845, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.843, test=0.576) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.839, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.839, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.848, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.836, test=0.590) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.832, test=0.553) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.840, test=0.580) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.840, test=0.583) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.843, test=0.567) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.845, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.843, test=0.576) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.839, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.839, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.848, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.836, test=0.590) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.832, test=0.553) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.840, test=0.580) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.840, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.843, test=0.567) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.845, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.843, test=0.576) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.839, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.839, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.848, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.836, test=0.591) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.832, test=0.553) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.840, test=0.580) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.840, test=0.583) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.843, test=0.567) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.845, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.843, test=0.576) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.839, test=0.557) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.839, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.848, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.831, test=0.590) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.832, test=0.553) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.839, test=0.581) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.837, test=0.585) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.843, test=0.567) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.843, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.841, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.836, test=0.559) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.835, test=0.567) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.845, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.829, test=0.591) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.831, test=0.554) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.839, test=0.581) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.837, test=0.585) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.842, test=0.567) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.843, test=0.553) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.840, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.836, test=0.559) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.835, test=0.567) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.845, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.829, test=0.592) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.830, test=0.554) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.837, test=0.582) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.836, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.841, test=0.569) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.842, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.839, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.835, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.835, test=0.567) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.845, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.829, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.829, test=0.557) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.837, test=0.582) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.836, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.841, test=0.569) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.842, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.839, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.835, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.835, test=0.567) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.845, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.829, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.829, test=0.557) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.837, test=0.582) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.836, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.841, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.842, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.839, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.835, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.835, test=0.567) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.845, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.829, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.829, test=0.557) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.837, test=0.582) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.836, test=0.585) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.841, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.842, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.838, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.835, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.833, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.845, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.829, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.829, test=0.557) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.837, test=0.582) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.836, test=0.585) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.841, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.842, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.837, test=0.576) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.834, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.833, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.844, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.829, test=0.592) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.829, test=0.557) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.837, test=0.582) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.836, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.841, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.842, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.837, test=0.576) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.834, test=0.561) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.833, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.844, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.829, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.829, test=0.557) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.837, test=0.582) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.834, test=0.587) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.839, test=0.568) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.842, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.837, test=0.576) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.834, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.833, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.844, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.829, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.829, test=0.557) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.837, test=0.582) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.834, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.839, test=0.568) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.842, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.837, test=0.576) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.834, test=0.561) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.833, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.844, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.829, test=0.592) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.829, test=0.557) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.837, test=0.582) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.834, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.839, test=0.568) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.841, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.837, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.834, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.833, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.844, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.828, test=0.594) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.828, test=0.558) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.837, test=0.583) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.834, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.839, test=0.568) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.840, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.837, test=0.578) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.834, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.833, test=0.568) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.844, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.828, test=0.594) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.828, test=0.558) total time=   0.1s
[CV 1/10] END ccp_alpha=0.000123115468409586;, score=(train=0.837, test=0.583) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000123115468409586;, score=(train=0.833, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000123115468409586;, score=(train=0.839, test=0.568) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000123115468409586;, score=(train=0.840, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.000123115468409586;, score=(train=0.837, test=0.578) total time=   0.2s
[CV 6/10] END ccp_alpha=0.000123115468409586;, score=(train=0.833, test=0.563) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000123115468409586;, score=(train=0.833, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000123115468409586;, score=(train=0.844, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.000123115468409586;, score=(train=0.828, test=0.594) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000123115468409586;, score=(train=0.828, test=0.558) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.837, test=0.583) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.833, test=0.590) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.838, test=0.568) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.840, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.835, test=0.576) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.833, test=0.563) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.832, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.843, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.828, test=0.594) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.828, test=0.558) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.833, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.831, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.835, test=0.569) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.838, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.834, test=0.575) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.831, test=0.565) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.832, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.842, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.827, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.828, test=0.558) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.830, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.830, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.835, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.838, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.834, test=0.575) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.831, test=0.565) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.832, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.842, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.826, test=0.596) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.827, test=0.559) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.830, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.830, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.835, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.838, test=0.552) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.834, test=0.575) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.831, test=0.565) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.832, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.842, test=0.583) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.826, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.827, test=0.559) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.830, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.829, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.835, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.838, test=0.552) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.834, test=0.575) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.830, test=0.562) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.832, test=0.569) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.841, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.826, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.825, test=0.561) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.829, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.829, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.835, test=0.569) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.838, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.834, test=0.575) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.830, test=0.562) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.832, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.841, test=0.583) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.826, test=0.596) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.825, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.829, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.828, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.835, test=0.569) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.838, test=0.553) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.834, test=0.575) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.830, test=0.562) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.832, test=0.569) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.841, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.826, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.825, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.829, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.828, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.835, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.838, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.834, test=0.575) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.830, test=0.562) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.832, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.841, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.826, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.825, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.829, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.827, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.834, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.837, test=0.553) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.830, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.830, test=0.562) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.832, test=0.569) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.841, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.826, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.825, test=0.560) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.829, test=0.583) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.826, test=0.588) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.834, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.836, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.830, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.829, test=0.562) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.831, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.840, test=0.583) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.826, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.825, test=0.560) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.829, test=0.583) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.826, test=0.588) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.834, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.836, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.830, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.829, test=0.562) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.831, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.840, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.825, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.825, test=0.560) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.826, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.825, test=0.588) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.832, test=0.570) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.836, test=0.555) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.830, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.829, test=0.562) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.827, test=0.566) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.840, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.824, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.823, test=0.559) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000125;, score=(train=0.826, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.000125;, score=(train=0.824, test=0.588) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000125;, score=(train=0.831, test=0.571) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000125;, score=(train=0.835, test=0.555) total time=   0.2s
[CV 5/10] END ccp_alpha=0.000125;, score=(train=0.828, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000125;, score=(train=0.828, test=0.564) total time=   0.2s
[CV 7/10] END ccp_alpha=0.000125;, score=(train=0.825, test=0.567) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000125;, score=(train=0.839, test=0.585) total time=   0.2s
[CV 9/10] END ccp_alpha=0.000125;, score=(train=0.823, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000125;, score=(train=0.822, test=0.559) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000125;, score=(train=0.826, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.000125;, score=(train=0.824, test=0.588) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000125;, score=(train=0.831, test=0.571) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000125;, score=(train=0.835, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.000125;, score=(train=0.828, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000125;, score=(train=0.828, test=0.564) total time=   0.2s
[CV 7/10] END ccp_alpha=0.000125;, score=(train=0.825, test=0.567) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000125;, score=(train=0.839, test=0.585) total time=   0.2s
[CV 9/10] END ccp_alpha=0.000125;, score=(train=0.823, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000125;, score=(train=0.822, test=0.559) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.825, test=0.583) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.823, test=0.589) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.830, test=0.570) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.832, test=0.557) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.827, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.827, test=0.565) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.824, test=0.567) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.837, test=0.587) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.822, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.821, test=0.561) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.822, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.818, test=0.588) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.829, test=0.572) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.832, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.825, test=0.578) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.826, test=0.565) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.823, test=0.567) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.837, test=0.588) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.822, test=0.597) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.817, test=0.562) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000125664300064556;, score=(train=0.822, test=0.583) total time=   0.2s
[CV 2/10] END ccp_alpha=0.000125664300064556;, score=(train=0.818, test=0.588) total time=   0.2s
[CV 3/10] END ccp_alpha=0.000125664300064556;, score=(train=0.829, test=0.572) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000125664300064556;, score=(train=0.830, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.000125664300064556;, score=(train=0.824, test=0.582) total time=   0.2s
[CV 6/10] END ccp_alpha=0.000125664300064556;, score=(train=0.826, test=0.566) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000125664300064556;, score=(train=0.822, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000125664300064556;, score=(train=0.836, test=0.588) total time=   0.2s
[CV 9/10] END ccp_alpha=0.000125664300064556;, score=(train=0.821, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000125664300064556;, score=(train=0.816, test=0.562) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.820, test=0.583) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.818, test=0.588) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.828, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.828, test=0.555) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.824, test=0.583) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.823, test=0.570) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.822, test=0.568) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.836, test=0.588) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.820, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.816, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.820, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.818, test=0.588) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.826, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.828, test=0.555) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.824, test=0.583) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.822, test=0.570) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.821, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.836, test=0.588) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.820, test=0.597) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.816, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.819, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.818, test=0.588) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.826, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.828, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.824, test=0.583) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.822, test=0.570) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.821, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.835, test=0.587) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.818, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.816, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.819, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.817, test=0.588) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.826, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.828, test=0.555) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.824, test=0.583) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.822, test=0.571) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.821, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.834, test=0.587) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.818, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.816, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.819, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.817, test=0.588) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.826, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.828, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.824, test=0.583) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.822, test=0.571) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.821, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.834, test=0.587) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.818, test=0.598) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.816, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.819, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.814, test=0.586) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.825, test=0.572) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.827, test=0.556) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.822, test=0.583) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.822, test=0.570) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.815, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.834, test=0.587) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.817, test=0.598) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.816, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.819, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.814, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.825, test=0.572) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.827, test=0.555) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.822, test=0.583) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.822, test=0.570) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.815, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.834, test=0.587) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.817, test=0.598) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.816, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.817, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.814, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.825, test=0.572) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.827, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.822, test=0.583) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.822, test=0.570) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.815, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.834, test=0.587) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.817, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.816, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.817, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.814, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.825, test=0.572) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.827, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.822, test=0.583) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.822, test=0.570) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.815, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.834, test=0.587) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.817, test=0.597) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.815, test=0.560) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.817, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.814, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.825, test=0.572) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.827, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.821, test=0.582) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.822, test=0.570) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.815, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.833, test=0.588) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.817, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.815, test=0.560) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.817, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.813, test=0.587) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.825, test=0.572) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.825, test=0.556) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.821, test=0.582) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.821, test=0.570) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.815, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.833, test=0.588) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.816, test=0.596) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.815, test=0.560) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.816, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.813, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.825, test=0.572) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.825, test=0.556) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.821, test=0.582) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.821, test=0.570) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.815, test=0.568) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.832, test=0.588) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.816, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.812, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.813, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.813, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.825, test=0.572) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.825, test=0.556) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.821, test=0.582) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.821, test=0.570) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.814, test=0.568) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.832, test=0.588) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.816, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.811, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.813, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.811, test=0.588) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.824, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.824, test=0.555) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.821, test=0.582) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.819, test=0.572) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.814, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.832, test=0.588) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.815, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.811, test=0.562) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.813, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.811, test=0.588) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.824, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.824, test=0.555) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.821, test=0.582) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.819, test=0.571) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.814, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.832, test=0.588) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.815, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.811, test=0.562) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.813, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.811, test=0.588) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.824, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.823, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.821, test=0.582) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.819, test=0.572) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.814, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.831, test=0.588) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.815, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.811, test=0.562) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.813, test=0.583) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.809, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.824, test=0.574) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.820, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.820, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.817, test=0.573) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.812, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.831, test=0.587) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.814, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.811, test=0.563) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.813, test=0.583) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.808, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.824, test=0.574) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.817, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.819, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.817, test=0.573) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.811, test=0.570) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.830, test=0.588) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.813, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.810, test=0.565) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.812, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.806, test=0.589) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.823, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.815, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.819, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.811, test=0.576) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.811, test=0.570) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.829, test=0.588) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.812, test=0.599) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.810, test=0.565) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.812, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.806, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.823, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.815, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.819, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.811, test=0.576) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.811, test=0.570) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.829, test=0.588) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.812, test=0.599) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.810, test=0.565) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.811, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.806, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.823, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.815, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.819, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.811, test=0.576) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.811, test=0.570) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.825, test=0.587) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.812, test=0.599) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.810, test=0.565) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.811, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.806, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.823, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.815, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.819, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.811, test=0.576) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.811, test=0.570) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.825, test=0.587) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.811, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.810, test=0.565) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.811, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.806, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.823, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.814, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.819, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.810, test=0.575) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.811, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.824, test=0.586) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.811, test=0.598) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.809, test=0.566) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.808, test=0.583) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.806, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.823, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.814, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.818, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.810, test=0.575) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.810, test=0.570) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.824, test=0.586) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.811, test=0.598) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.809, test=0.566) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.808, test=0.583) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.806, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.823, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.814, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.818, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.810, test=0.575) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.810, test=0.570) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.824, test=0.586) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.811, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.809, test=0.566) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000129025851737716;, score=(train=0.808, test=0.583) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000129025851737716;, score=(train=0.806, test=0.589) total time=   0.2s
[CV 3/10] END ccp_alpha=0.000129025851737716;, score=(train=0.823, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000129025851737716;, score=(train=0.814, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.000129025851737716;, score=(train=0.818, test=0.583) total time=   0.2s
[CV 6/10] END ccp_alpha=0.000129025851737716;, score=(train=0.810, test=0.575) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000129025851737716;, score=(train=0.810, test=0.570) total time=   0.2s
[CV 8/10] END ccp_alpha=0.000129025851737716;, score=(train=0.823, test=0.587) total time=   0.1s
[CV 9/10] END ccp_alpha=0.000129025851737716;, score=(train=0.811, test=0.598) total time=   0.2s
[CV 10/10] END ccp_alpha=0.000129025851737716;, score=(train=0.808, test=0.567) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.804, test=0.583) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.805, test=0.590) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.823, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.814, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.817, test=0.582) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.809, test=0.576) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.810, test=0.570) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.820, test=0.587) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.811, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.808, test=0.567) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.804, test=0.583) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.805, test=0.590) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.823, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.814, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.817, test=0.582) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.809, test=0.576) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.810, test=0.570) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.820, test=0.587) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.811, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.808, test=0.567) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.802, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.805, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.820, test=0.576) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.813, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.815, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.809, test=0.576) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.809, test=0.570) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.820, test=0.587) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.810, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.808, test=0.567) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.802, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.805, test=0.590) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.820, test=0.576) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.813, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.815, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.809, test=0.576) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.809, test=0.570) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.820, test=0.587) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.810, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.808, test=0.567) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.802, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.804, test=0.591) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.820, test=0.576) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.813, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.815, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.807, test=0.579) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.809, test=0.570) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.819, test=0.587) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.809, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.807, test=0.566) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000129814921920185;, score=(train=0.801, test=0.586) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000129814921920185;, score=(train=0.804, test=0.591) total time=   0.2s
[CV 3/10] END ccp_alpha=0.000129814921920185;, score=(train=0.819, test=0.576) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000129814921920185;, score=(train=0.812, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.000129814921920185;, score=(train=0.814, test=0.580) total time=   0.2s
[CV 6/10] END ccp_alpha=0.000129814921920185;, score=(train=0.806, test=0.581) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000129814921920185;, score=(train=0.807, test=0.572) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000129814921920185;, score=(train=0.816, test=0.586) total time=   0.1s
[CV 9/10] END ccp_alpha=0.000129814921920185;, score=(train=0.808, test=0.596) total time=   0.2s
[CV 10/10] END ccp_alpha=0.000129814921920185;, score=(train=0.807, test=0.566) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.801, test=0.586) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.804, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.819, test=0.576) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.812, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.814, test=0.580) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.806, test=0.581) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.807, test=0.572) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.816, test=0.586) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.807, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.807, test=0.566) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.801, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.803, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.817, test=0.577) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.812, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.814, test=0.579) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.805, test=0.580) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.807, test=0.572) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.815, test=0.586) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.806, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.805, test=0.568) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.801, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.803, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.817, test=0.577) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.812, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.813, test=0.582) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.804, test=0.580) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.807, test=0.572) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.815, test=0.586) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.806, test=0.596) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.805, test=0.568) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.801, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.803, test=0.590) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.817, test=0.577) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.812, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.813, test=0.582) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.804, test=0.580) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.807, test=0.572) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.814, test=0.585) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.806, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.804, test=0.569) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.801, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.803, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.816, test=0.577) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.812, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.812, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.802, test=0.583) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.806, test=0.574) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.814, test=0.584) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.806, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.804, test=0.569) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.801, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.803, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.816, test=0.577) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.812, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.812, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.802, test=0.584) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.806, test=0.574) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.814, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.806, test=0.596) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.804, test=0.568) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.801, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.803, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.816, test=0.577) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.812, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.812, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.802, test=0.584) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.806, test=0.574) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.814, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.806, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.804, test=0.568) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.800, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.803, test=0.589) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.816, test=0.578) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.812, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.812, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.802, test=0.584) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.806, test=0.574) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.814, test=0.583) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.805, test=0.597) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.804, test=0.568) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.800, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.803, test=0.589) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.816, test=0.578) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.812, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.812, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.802, test=0.584) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.805, test=0.576) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.814, test=0.583) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.805, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.804, test=0.568) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.800, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.803, test=0.589) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.815, test=0.576) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.811, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.811, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.802, test=0.584) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.805, test=0.576) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.813, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.802, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.803, test=0.568) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.799, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.802, test=0.591) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.815, test=0.576) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.810, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.810, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.801, test=0.583) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.804, test=0.576) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.813, test=0.583) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.802, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.803, test=0.568) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.799, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.802, test=0.591) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.815, test=0.576) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.810, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.810, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.801, test=0.583) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.804, test=0.576) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.813, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.802, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.803, test=0.568) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.799, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.802, test=0.591) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.815, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.810, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.810, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.801, test=0.585) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.804, test=0.576) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.813, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.802, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.803, test=0.568) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.799, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.802, test=0.591) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.814, test=0.576) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.809, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.810, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.801, test=0.585) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.804, test=0.576) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.813, test=0.583) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.802, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.803, test=0.568) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.799, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.802, test=0.591) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.814, test=0.576) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.809, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.810, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.801, test=0.585) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.803, test=0.575) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.813, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.802, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.803, test=0.568) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.799, test=0.586) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.802, test=0.591) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.813, test=0.576) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.809, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.810, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.801, test=0.586) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.803, test=0.575) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.813, test=0.583) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.801, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.803, test=0.568) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.796, test=0.586) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.800, test=0.590) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.811, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.808, test=0.554) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.803, test=0.577) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.798, test=0.587) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.802, test=0.576) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.811, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.798, test=0.594) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.800, test=0.570) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.796, test=0.587) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.800, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.811, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.807, test=0.555) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.803, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.798, test=0.587) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.802, test=0.576) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.811, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.795, test=0.594) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.800, test=0.570) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.795, test=0.587) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.800, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.811, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.807, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.803, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.798, test=0.587) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.802, test=0.576) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.811, test=0.580) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.795, test=0.594) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.800, test=0.570) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.795, test=0.587) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.800, test=0.590) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.811, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.807, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.803, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.798, test=0.587) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.802, test=0.576) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.811, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.795, test=0.594) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.800, test=0.570) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.793, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.800, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.811, test=0.576) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.805, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.801, test=0.578) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.797, test=0.586) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.802, test=0.576) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.809, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.794, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.799, test=0.569) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.792, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.800, test=0.590) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.810, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.805, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.800, test=0.578) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.797, test=0.586) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.802, test=0.576) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.809, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.794, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.799, test=0.569) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.791, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.800, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.809, test=0.574) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.805, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.799, test=0.578) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.797, test=0.586) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.800, test=0.578) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.808, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.792, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.798, test=0.569) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.791, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.799, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.809, test=0.574) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.805, test=0.556) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.799, test=0.580) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.796, test=0.587) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.800, test=0.578) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.808, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.791, test=0.593) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.798, test=0.571) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.791, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.799, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.809, test=0.574) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.804, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.799, test=0.580) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.796, test=0.587) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.800, test=0.578) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.808, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.791, test=0.593) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.798, test=0.571) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.791, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.798, test=0.591) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.809, test=0.574) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.804, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.799, test=0.580) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.796, test=0.587) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.799, test=0.578) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.808, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.791, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.797, test=0.571) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.791, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.798, test=0.591) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.809, test=0.574) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.804, test=0.557) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.799, test=0.580) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.796, test=0.587) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.798, test=0.578) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.807, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.790, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.797, test=0.572) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.791, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.798, test=0.591) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.809, test=0.574) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.804, test=0.557) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.799, test=0.580) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.796, test=0.587) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.798, test=0.578) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.806, test=0.580) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.790, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.797, test=0.572) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.789, test=0.586) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.797, test=0.593) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.806, test=0.576) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.803, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.799, test=0.580) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.794, test=0.588) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.798, test=0.578) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.805, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.787, test=0.593) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.797, test=0.571) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.789, test=0.586) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.797, test=0.593) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.806, test=0.576) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.803, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.799, test=0.580) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.794, test=0.588) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.797, test=0.579) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.805, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.786, test=0.590) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.797, test=0.571) total time=   0.1s
[CV 1/10] END ccp_alpha=0.000134248555281302;, score=(train=0.789, test=0.586) total time=   0.2s
[CV 2/10] END ccp_alpha=0.000134248555281302;, score=(train=0.795, test=0.592) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000134248555281302;, score=(train=0.804, test=0.574) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000134248555281302;, score=(train=0.803, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.000134248555281302;, score=(train=0.797, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000134248555281302;, score=(train=0.792, test=0.586) total time=   0.2s
[CV 7/10] END ccp_alpha=0.000134248555281302;, score=(train=0.797, test=0.579) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000134248555281302;, score=(train=0.804, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.000134248555281302;, score=(train=0.782, test=0.590) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000134248555281302;, score=(train=0.796, test=0.572) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.787, test=0.590) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.791, test=0.596) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.803, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.800, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.796, test=0.580) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.788, test=0.588) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.794, test=0.578) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.798, test=0.574) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.777, test=0.591) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.793, test=0.574) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.787, test=0.590) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.791, test=0.596) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.803, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.800, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.796, test=0.580) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.788, test=0.588) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.794, test=0.578) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.797, test=0.574) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.777, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.793, test=0.574) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.787, test=0.590) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.791, test=0.596) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.803, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.800, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.796, test=0.580) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.788, test=0.588) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.794, test=0.578) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.797, test=0.574) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.777, test=0.591) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.793, test=0.574) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.787, test=0.590) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.791, test=0.596) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.803, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.800, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.796, test=0.580) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.788, test=0.588) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.794, test=0.578) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.797, test=0.574) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.777, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.793, test=0.574) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.787, test=0.590) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.788, test=0.596) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.802, test=0.574) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.800, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.793, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.788, test=0.589) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.792, test=0.577) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.797, test=0.574) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.775, test=0.593) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.792, test=0.574) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.785, test=0.588) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.788, test=0.596) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.802, test=0.574) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.799, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.793, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.788, test=0.589) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.792, test=0.577) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.797, test=0.574) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.775, test=0.593) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.791, test=0.575) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.784, test=0.588) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.787, test=0.596) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.802, test=0.574) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.799, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.793, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.788, test=0.589) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.792, test=0.577) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.797, test=0.574) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.774, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.791, test=0.577) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.784, test=0.588) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.787, test=0.596) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.802, test=0.574) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.799, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.793, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.787, test=0.590) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.792, test=0.577) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.797, test=0.574) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.774, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.791, test=0.577) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.783, test=0.590) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.787, test=0.596) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.801, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.799, test=0.557) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.792, test=0.582) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.787, test=0.590) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.792, test=0.577) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.797, test=0.574) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.774, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.791, test=0.577) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.777, test=0.593) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.786, test=0.595) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.799, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.794, test=0.556) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.790, test=0.584) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.785, test=0.592) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.792, test=0.578) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.795, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.770, test=0.592) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.787, test=0.577) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.775, test=0.592) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.784, test=0.594) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.795, test=0.571) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.793, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.790, test=0.584) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.785, test=0.592) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.791, test=0.578) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.793, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.770, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.787, test=0.577) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.775, test=0.592) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.784, test=0.595) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.794, test=0.571) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.793, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.790, test=0.584) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.785, test=0.592) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.790, test=0.579) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.793, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.770, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.787, test=0.578) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.774, test=0.592) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.783, test=0.597) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.789, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.793, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.790, test=0.584) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.784, test=0.593) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.785, test=0.579) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.793, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.769, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.786, test=0.575) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.774, test=0.592) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.782, test=0.598) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.787, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.793, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.789, test=0.584) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.784, test=0.593) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.784, test=0.581) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.792, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.769, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.786, test=0.575) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.774, test=0.594) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.781, test=0.597) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.786, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.791, test=0.559) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.789, test=0.584) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.783, test=0.592) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.783, test=0.582) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.791, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.767, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.784, test=0.576) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.774, test=0.594) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.781, test=0.598) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.786, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.791, test=0.559) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.789, test=0.584) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.782, test=0.593) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.783, test=0.582) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.790, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.767, test=0.598) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.784, test=0.576) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.774, test=0.594) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.781, test=0.598) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.786, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.791, test=0.559) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.789, test=0.584) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.782, test=0.593) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.783, test=0.582) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.789, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.767, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.784, test=0.576) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.774, test=0.594) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.781, test=0.598) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.786, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.791, test=0.559) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.789, test=0.584) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.782, test=0.593) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.783, test=0.582) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.789, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.767, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.784, test=0.576) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.773, test=0.594) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.780, test=0.598) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.783, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.790, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.788, test=0.584) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.782, test=0.595) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.782, test=0.582) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.788, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.766, test=0.597) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.784, test=0.577) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.773, test=0.594) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.779, test=0.598) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.783, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.790, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.786, test=0.585) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.782, test=0.595) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.782, test=0.582) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.788, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.766, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.783, test=0.578) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.770, test=0.597) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.778, test=0.597) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.782, test=0.572) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.790, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.784, test=0.586) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.780, test=0.596) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.782, test=0.581) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.785, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.765, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.782, test=0.580) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.770, test=0.597) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.778, test=0.597) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.782, test=0.572) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.790, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.784, test=0.586) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.780, test=0.596) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.782, test=0.581) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.785, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.765, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.782, test=0.580) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.768, test=0.597) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.778, test=0.597) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.782, test=0.572) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.790, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.784, test=0.586) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.780, test=0.596) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.782, test=0.581) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.785, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.765, test=0.594) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.782, test=0.580) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.768, test=0.597) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.778, test=0.597) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.782, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.790, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.782, test=0.587) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.780, test=0.596) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.781, test=0.581) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.785, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.764, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.781, test=0.580) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.768, test=0.597) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.778, test=0.597) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.782, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.790, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.782, test=0.587) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.780, test=0.596) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.781, test=0.581) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.785, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.764, test=0.595) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.781, test=0.580) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.768, test=0.597) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.778, test=0.597) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.782, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.790, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.782, test=0.587) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.780, test=0.596) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.781, test=0.580) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.785, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.764, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.780, test=0.579) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.768, test=0.597) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.778, test=0.597) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.782, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.790, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.782, test=0.587) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.780, test=0.596) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.781, test=0.580) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.785, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.764, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.780, test=0.579) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.768, test=0.597) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.778, test=0.597) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.782, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.790, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.782, test=0.587) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.780, test=0.596) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.781, test=0.579) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.785, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.764, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.780, test=0.579) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.768, test=0.597) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.778, test=0.597) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.782, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.790, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.782, test=0.587) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.780, test=0.596) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.780, test=0.579) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.785, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.764, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.780, test=0.579) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.767, test=0.597) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.777, test=0.596) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.780, test=0.574) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.787, test=0.556) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.781, test=0.586) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.780, test=0.595) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.779, test=0.580) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.784, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.763, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.780, test=0.578) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.765, test=0.597) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.776, test=0.595) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.777, test=0.574) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.787, test=0.556) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.781, test=0.586) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.780, test=0.594) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.779, test=0.580) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.783, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.763, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.779, test=0.578) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.765, test=0.597) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.775, test=0.595) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.777, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.785, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.780, test=0.586) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.780, test=0.594) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.777, test=0.581) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.782, test=0.584) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.762, test=0.597) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.779, test=0.578) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.764, test=0.598) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.774, test=0.595) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.777, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.785, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.780, test=0.586) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.780, test=0.594) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.777, test=0.581) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.782, test=0.584) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.762, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.779, test=0.578) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.764, test=0.598) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.774, test=0.595) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.776, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.785, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.780, test=0.586) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.780, test=0.594) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.777, test=0.582) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.782, test=0.584) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.762, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.779, test=0.578) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.764, test=0.598) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.774, test=0.595) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.775, test=0.577) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.784, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.779, test=0.585) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.779, test=0.593) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.776, test=0.582) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.780, test=0.584) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.761, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.778, test=0.578) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.763, test=0.599) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.774, test=0.595) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.775, test=0.577) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.780, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.778, test=0.586) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.779, test=0.594) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.776, test=0.583) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.780, test=0.584) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.760, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.778, test=0.579) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.763, test=0.599) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.774, test=0.595) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.775, test=0.576) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.780, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.778, test=0.586) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.779, test=0.594) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.773, test=0.583) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.779, test=0.584) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.760, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.778, test=0.579) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.763, test=0.599) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.774, test=0.595) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.775, test=0.576) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.780, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.778, test=0.586) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.779, test=0.594) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.773, test=0.585) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.779, test=0.584) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.760, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.778, test=0.578) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.763, test=0.599) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.773, test=0.595) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.775, test=0.576) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.778, test=0.552) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.778, test=0.586) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.777, test=0.594) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.773, test=0.585) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.779, test=0.584) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.760, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.778, test=0.578) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.763, test=0.599) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.773, test=0.594) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.775, test=0.576) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.777, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.775, test=0.587) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.777, test=0.594) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.769, test=0.589) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.777, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.760, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.777, test=0.578) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.762, test=0.599) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.773, test=0.594) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.775, test=0.576) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.777, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.775, test=0.587) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.776, test=0.595) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.768, test=0.589) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.777, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.759, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.776, test=0.578) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000142948717948718;, score=(train=0.762, test=0.599) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000142948717948718;, score=(train=0.773, test=0.594) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000142948717948718;, score=(train=0.774, test=0.578) total time=   0.1s
[CV 4/10] END ccp_alpha=0.000142948717948718;, score=(train=0.777, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.000142948717948718;, score=(train=0.775, test=0.587) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000142948717948718;, score=(train=0.776, test=0.595) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000142948717948718;, score=(train=0.768, test=0.589) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000142948717948718;, score=(train=0.777, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.000142948717948718;, score=(train=0.759, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000142948717948718;, score=(train=0.776, test=0.578) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.762, test=0.598) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.773, test=0.594) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.772, test=0.579) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.776, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.774, test=0.587) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.776, test=0.595) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.767, test=0.588) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.773, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.756, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.774, test=0.577) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.761, test=0.600) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.773, test=0.594) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.772, test=0.579) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.776, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.774, test=0.587) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.776, test=0.595) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.767, test=0.588) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.773, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.756, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.774, test=0.577) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.758, test=0.602) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.773, test=0.594) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.772, test=0.577) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.776, test=0.554) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.773, test=0.588) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.774, test=0.597) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.767, test=0.588) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.773, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.755, test=0.596) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.774, test=0.579) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.758, test=0.602) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.773, test=0.594) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.771, test=0.577) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.775, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.773, test=0.588) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.774, test=0.597) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.767, test=0.588) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.770, test=0.585) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.754, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.773, test=0.579) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.758, test=0.602) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.772, test=0.595) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.771, test=0.577) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.775, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.773, test=0.588) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.774, test=0.597) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.765, test=0.588) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.768, test=0.583) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.753, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.773, test=0.579) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.758, test=0.602) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.772, test=0.595) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.771, test=0.577) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.775, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.773, test=0.588) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.774, test=0.597) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.765, test=0.588) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.768, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.753, test=0.597) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.773, test=0.579) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.758, test=0.601) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.770, test=0.595) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.771, test=0.578) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.775, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.773, test=0.588) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.773, test=0.598) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.765, test=0.588) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.768, test=0.585) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.751, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.773, test=0.579) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.757, test=0.603) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.769, test=0.593) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.769, test=0.579) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.774, test=0.556) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.772, test=0.589) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.763, test=0.604) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.764, test=0.588) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.764, test=0.585) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.751, test=0.596) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.770, test=0.582) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.757, test=0.603) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.769, test=0.593) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.768, test=0.580) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.773, test=0.556) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.772, test=0.589) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.762, test=0.604) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.764, test=0.588) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.764, test=0.585) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.751, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.769, test=0.584) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.757, test=0.603) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.769, test=0.593) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.768, test=0.580) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.772, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.772, test=0.590) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.762, test=0.604) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.763, test=0.588) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.763, test=0.585) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.748, test=0.599) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.769, test=0.584) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.757, test=0.603) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.768, test=0.593) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.768, test=0.580) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.772, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.772, test=0.590) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.762, test=0.604) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.763, test=0.588) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.763, test=0.585) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.748, test=0.599) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.769, test=0.584) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.756, test=0.602) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.768, test=0.593) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.767, test=0.580) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.770, test=0.560) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.767, test=0.595) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.762, test=0.604) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.762, test=0.592) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.760, test=0.584) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.747, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.767, test=0.588) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.755, test=0.602) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.766, test=0.594) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.767, test=0.580) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.770, test=0.560) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.767, test=0.595) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.761, test=0.605) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.760, test=0.593) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.760, test=0.584) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.747, test=0.602) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.767, test=0.588) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.755, test=0.602) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.765, test=0.595) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.766, test=0.580) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.770, test=0.560) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.763, test=0.596) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.760, test=0.604) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.759, test=0.591) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.760, test=0.584) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.743, test=0.605) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.767, test=0.588) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.755, test=0.602) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.764, test=0.594) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.766, test=0.582) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.769, test=0.561) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.761, test=0.597) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.760, test=0.606) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.758, test=0.592) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.760, test=0.584) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.742, test=0.606) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.766, test=0.587) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.753, test=0.603) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.762, test=0.597) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.759, test=0.582) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.768, test=0.562) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.757, test=0.599) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.759, test=0.607) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.756, test=0.595) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.755, test=0.586) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.740, test=0.603) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.761, test=0.589) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.753, test=0.603) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.762, test=0.597) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.759, test=0.582) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.765, test=0.560) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.757, test=0.599) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.758, test=0.607) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.754, test=0.597) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.755, test=0.586) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.740, test=0.603) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.761, test=0.589) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.748, test=0.601) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.759, test=0.600) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.756, test=0.580) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.759, test=0.562) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.751, test=0.606) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.756, test=0.605) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.751, test=0.598) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.750, test=0.584) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.736, test=0.605) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.758, test=0.591) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.748, test=0.601) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.759, test=0.599) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.756, test=0.580) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.759, test=0.562) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.751, test=0.606) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.756, test=0.604) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.751, test=0.598) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.750, test=0.584) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.736, test=0.605) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.758, test=0.591) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.747, test=0.601) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.757, test=0.601) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.754, test=0.581) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.755, test=0.566) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.749, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.755, test=0.603) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.747, test=0.594) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.748, test=0.585) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.735, test=0.604) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.750, test=0.590) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.747, test=0.601) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.757, test=0.601) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.754, test=0.581) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.755, test=0.566) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.749, test=0.608) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.755, test=0.603) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.747, test=0.594) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.748, test=0.585) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.735, test=0.604) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.750, test=0.590) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.747, test=0.602) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.755, test=0.603) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.754, test=0.581) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.755, test=0.566) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.749, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.750, test=0.609) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.747, test=0.595) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.747, test=0.585) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.735, test=0.604) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.750, test=0.590) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.746, test=0.602) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.755, test=0.603) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.754, test=0.580) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.755, test=0.566) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.749, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.750, test=0.609) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.747, test=0.595) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.747, test=0.585) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.734, test=0.604) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.750, test=0.590) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.746, test=0.602) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.755, test=0.603) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.754, test=0.580) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.755, test=0.565) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.748, test=0.607) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.750, test=0.609) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.747, test=0.595) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.746, test=0.586) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.734, test=0.604) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.750, test=0.590) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.745, test=0.603) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.752, test=0.603) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.744, test=0.587) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.754, test=0.566) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.748, test=0.607) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.748, test=0.611) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.746, test=0.595) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.741, test=0.589) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.733, test=0.603) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.749, test=0.589) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.744, test=0.603) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.752, test=0.603) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.744, test=0.587) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.753, test=0.567) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.748, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.747, test=0.611) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.746, test=0.595) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.739, test=0.590) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.733, test=0.603) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.749, test=0.589) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.737, test=0.604) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.751, test=0.602) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.743, test=0.587) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.749, test=0.569) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.747, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.744, test=0.611) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.744, test=0.597) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.738, test=0.591) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.733, test=0.603) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.747, test=0.591) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.736, test=0.605) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.747, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.742, test=0.588) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.747, test=0.570) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.747, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.744, test=0.611) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.744, test=0.597) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.737, test=0.591) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.732, test=0.604) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.745, test=0.591) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.735, test=0.606) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.745, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.738, test=0.585) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.744, test=0.573) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.746, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.740, test=0.612) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.742, test=0.598) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.732, test=0.593) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.729, test=0.605) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.743, test=0.595) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.735, test=0.605) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.742, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.738, test=0.585) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.744, test=0.573) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.744, test=0.609) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.740, test=0.612) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.742, test=0.599) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.731, test=0.594) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.728, test=0.605) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.742, test=0.594) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.735, test=0.605) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.742, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.737, test=0.585) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.744, test=0.573) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.744, test=0.609) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.740, test=0.612) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.742, test=0.599) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.731, test=0.594) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.728, test=0.605) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.742, test=0.594) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.730, test=0.610) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.737, test=0.603) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.732, test=0.590) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.743, test=0.576) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.744, test=0.609) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.738, test=0.613) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.740, test=0.597) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.729, test=0.595) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.726, test=0.605) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.738, test=0.598) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.730, test=0.610) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.737, test=0.603) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.732, test=0.590) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.743, test=0.576) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.744, test=0.609) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.738, test=0.613) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.740, test=0.597) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.729, test=0.595) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.726, test=0.605) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.738, test=0.598) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.730, test=0.610) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.737, test=0.603) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.732, test=0.590) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.743, test=0.576) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.744, test=0.609) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.738, test=0.613) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.740, test=0.597) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.729, test=0.595) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.726, test=0.605) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.738, test=0.598) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.730, test=0.610) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.736, test=0.602) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.732, test=0.590) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.742, test=0.577) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.743, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.738, test=0.614) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.739, test=0.598) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.729, test=0.595) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.722, test=0.600) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.738, test=0.598) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.729, test=0.611) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.736, test=0.602) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.732, test=0.590) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.742, test=0.577) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.741, test=0.608) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.736, test=0.616) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.736, test=0.603) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.729, test=0.595) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.722, test=0.600) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.738, test=0.599) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.729, test=0.611) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.735, test=0.602) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.732, test=0.591) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.742, test=0.577) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.741, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.736, test=0.616) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.736, test=0.603) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.729, test=0.595) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.718, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.738, test=0.599) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.728, test=0.613) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.735, test=0.602) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.731, test=0.593) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.742, test=0.577) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.741, test=0.608) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.735, test=0.616) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.734, test=0.605) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.729, test=0.595) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.718, test=0.602) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.737, test=0.599) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.728, test=0.613) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.735, test=0.602) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.731, test=0.593) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.742, test=0.577) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.741, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.735, test=0.617) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.734, test=0.605) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.728, test=0.596) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.718, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.737, test=0.599) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.728, test=0.613) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.732, test=0.604) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.731, test=0.593) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.741, test=0.577) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.741, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.735, test=0.617) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.732, test=0.603) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.728, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.718, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.735, test=0.600) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.728, test=0.613) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.732, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.731, test=0.593) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.741, test=0.577) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.741, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.735, test=0.617) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.732, test=0.604) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.728, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.718, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.735, test=0.600) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.728, test=0.613) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.732, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.730, test=0.592) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.740, test=0.578) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.741, test=0.608) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.734, test=0.617) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.725, test=0.605) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.728, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.718, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.734, test=0.600) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.727, test=0.613) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.732, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.730, test=0.592) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.740, test=0.578) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.740, test=0.608) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.734, test=0.617) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.725, test=0.605) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.728, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.718, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.734, test=0.600) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.726, test=0.613) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.732, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.729, test=0.592) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.737, test=0.578) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.738, test=0.608) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.734, test=0.617) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.724, test=0.606) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.728, test=0.596) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.718, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.734, test=0.600) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.726, test=0.613) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.732, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.729, test=0.592) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.737, test=0.578) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.738, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.734, test=0.617) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.724, test=0.606) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.728, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.718, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.734, test=0.600) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.725, test=0.614) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.732, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.729, test=0.592) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.737, test=0.578) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.738, test=0.608) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.734, test=0.617) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.723, test=0.606) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.728, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.717, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.734, test=0.601) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.723, test=0.614) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.731, test=0.605) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.728, test=0.592) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.737, test=0.578) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.738, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.734, test=0.617) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.722, test=0.607) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.728, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.716, test=0.603) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.733, test=0.601) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.723, test=0.614) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.731, test=0.605) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.728, test=0.592) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.737, test=0.578) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.738, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.733, test=0.617) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.722, test=0.607) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.727, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.716, test=0.603) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.733, test=0.601) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.723, test=0.614) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.730, test=0.606) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.728, test=0.592) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.731, test=0.579) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.737, test=0.609) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.729, test=0.618) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.718, test=0.608) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.727, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.715, test=0.601) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.732, test=0.602) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.722, test=0.615) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.726, test=0.606) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.728, test=0.592) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.728, test=0.583) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.734, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.728, test=0.617) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.718, test=0.608) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.727, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.715, test=0.601) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.732, test=0.602) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.720, test=0.615) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.723, test=0.609) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.728, test=0.592) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.728, test=0.583) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.734, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.727, test=0.616) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.718, test=0.608) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.727, test=0.596) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.715, test=0.601) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.729, test=0.605) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.719, test=0.615) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.723, test=0.609) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.727, test=0.591) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.728, test=0.583) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.732, test=0.612) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.724, test=0.616) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.714, test=0.607) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.726, test=0.595) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.715, test=0.601) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.729, test=0.605) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.719, test=0.615) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.723, test=0.609) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.727, test=0.591) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.728, test=0.583) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.732, test=0.612) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.724, test=0.616) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.714, test=0.607) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.726, test=0.595) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.715, test=0.601) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.729, test=0.605) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.712, test=0.617) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.718, test=0.603) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.723, test=0.590) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.725, test=0.582) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.727, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.717, test=0.621) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.709, test=0.611) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.718, test=0.601) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.708, test=0.607) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.727, test=0.605) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.711, test=0.620) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.718, test=0.603) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.722, test=0.590) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.725, test=0.582) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.727, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.717, test=0.621) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.709, test=0.611) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.718, test=0.601) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.707, test=0.609) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.727, test=0.605) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.711, test=0.620) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.718, test=0.603) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.722, test=0.592) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.725, test=0.582) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.727, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.717, test=0.621) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.709, test=0.611) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.717, test=0.603) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.707, test=0.609) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.722, test=0.606) total time=   0.1s
[CV 1/10] END ccp_alpha=0.000164898975984408;, score=(train=0.711, test=0.620) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000164898975984408;, score=(train=0.718, test=0.603) total time=   0.2s
[CV 3/10] END ccp_alpha=0.000164898975984408;, score=(train=0.721, test=0.593) total time=   0.1s
[CV 4/10] END ccp_alpha=0.000164898975984408;, score=(train=0.725, test=0.581) total time=   0.2s
[CV 5/10] END ccp_alpha=0.000164898975984408;, score=(train=0.727, test=0.610) total time=   0.2s
[CV 6/10] END ccp_alpha=0.000164898975984408;, score=(train=0.717, test=0.621) total time=   0.2s
[CV 7/10] END ccp_alpha=0.000164898975984408;, score=(train=0.708, test=0.610) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000164898975984408;, score=(train=0.715, test=0.604) total time=   0.2s
[CV 9/10] END ccp_alpha=0.000164898975984408;, score=(train=0.707, test=0.609) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000164898975984408;, score=(train=0.721, test=0.608) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.710, test=0.619) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.718, test=0.603) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.721, test=0.593) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.724, test=0.582) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.727, test=0.610) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.717, test=0.621) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.708, test=0.610) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.715, test=0.604) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.707, test=0.609) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.721, test=0.608) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.710, test=0.619) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.716, test=0.603) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.719, test=0.591) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.722, test=0.582) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.727, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.716, test=0.623) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.707, test=0.610) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.715, test=0.604) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.706, test=0.610) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.721, test=0.608) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.710, test=0.620) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.715, test=0.603) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.710, test=0.595) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.718, test=0.583) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.725, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.716, test=0.623) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.707, test=0.610) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.714, test=0.602) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.705, test=0.612) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.717, test=0.610) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.710, test=0.620) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.714, test=0.603) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.710, test=0.597) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.718, test=0.583) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.724, test=0.609) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.716, test=0.623) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.707, test=0.610) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.713, test=0.601) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.704, test=0.612) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.714, test=0.609) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.709, test=0.621) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.714, test=0.603) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.709, test=0.598) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.716, test=0.585) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.724, test=0.609) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.713, test=0.626) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.704, test=0.613) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.713, test=0.602) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.704, test=0.612) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.713, test=0.609) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.706, test=0.622) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.711, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.703, test=0.603) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.713, test=0.588) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.723, test=0.612) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.710, test=0.624) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.701, test=0.614) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.705, test=0.608) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.699, test=0.616) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.710, test=0.610) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.704, test=0.622) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.710, test=0.604) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.701, test=0.604) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.712, test=0.588) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.719, test=0.616) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.708, test=0.626) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.701, test=0.614) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.702, test=0.613) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.699, test=0.616) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.700, test=0.615) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.701, test=0.626) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.708, test=0.605) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.700, test=0.607) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.711, test=0.591) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.715, test=0.618) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.708, test=0.627) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.701, test=0.614) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.700, test=0.614) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.699, test=0.617) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.695, test=0.619) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.701, test=0.626) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.705, test=0.607) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.699, test=0.608) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.711, test=0.591) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.715, test=0.618) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.707, test=0.628) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.701, test=0.614) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.693, test=0.618) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.697, test=0.617) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.694, test=0.619) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.695, test=0.628) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.703, test=0.611) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.699, test=0.609) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.709, test=0.591) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.710, test=0.620) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.707, test=0.627) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.700, test=0.612) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.686, test=0.621) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.695, test=0.616) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.692, test=0.619) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.691, test=0.629) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.702, test=0.611) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.698, test=0.610) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.708, test=0.590) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.706, test=0.619) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.699, test=0.632) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.696, test=0.612) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.685, test=0.623) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.690, test=0.616) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.689, test=0.622) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.690, test=0.628) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.696, test=0.617) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.690, test=0.606) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.702, test=0.595) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.704, test=0.618) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.698, test=0.632) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.693, test=0.619) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.684, test=0.622) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.685, test=0.621) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.685, test=0.623) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.690, test=0.628) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.696, test=0.615) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.683, test=0.618) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.702, test=0.595) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.697, test=0.619) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.697, test=0.633) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.687, test=0.627) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.682, test=0.624) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.685, test=0.621) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.685, test=0.623) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.690, test=0.628) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.696, test=0.614) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.681, test=0.621) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.701, test=0.595) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.696, test=0.619) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.695, test=0.632) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.686, test=0.628) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.682, test=0.624) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.685, test=0.622) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.685, test=0.625) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.690, test=0.628) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.693, test=0.619) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.679, test=0.622) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.700, test=0.595) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.696, test=0.621) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.695, test=0.632) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.684, test=0.630) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.680, test=0.624) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.683, test=0.620) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.685, test=0.625) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000184705754624993;, score=(train=0.689, test=0.630) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000184705754624993;, score=(train=0.693, test=0.619) total time=   0.2s
[CV 3/10] END ccp_alpha=0.000184705754624993;, score=(train=0.679, test=0.622) total time=   0.1s
[CV 4/10] END ccp_alpha=0.000184705754624993;, score=(train=0.693, test=0.606) total time=   0.2s
[CV 5/10] END ccp_alpha=0.000184705754624993;, score=(train=0.693, test=0.620) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000184705754624993;, score=(train=0.694, test=0.633) total time=   0.2s
[CV 7/10] END ccp_alpha=0.000184705754624993;, score=(train=0.680, test=0.629) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000184705754624993;, score=(train=0.680, test=0.624) total time=   0.2s
[CV 9/10] END ccp_alpha=0.000184705754624993;, score=(train=0.683, test=0.620) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000184705754624993;, score=(train=0.685, test=0.625) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.689, test=0.630) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.691, test=0.616) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.678, test=0.624) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.693, test=0.606) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.692, test=0.622) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.694, test=0.633) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.679, test=0.629) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.680, test=0.624) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.683, test=0.620) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.685, test=0.625) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.687, test=0.631) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.689, test=0.618) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.678, test=0.624) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.693, test=0.606) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.691, test=0.621) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.694, test=0.633) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.678, test=0.626) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.678, test=0.625) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.682, test=0.620) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.685, test=0.625) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.684, test=0.630) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.686, test=0.614) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.678, test=0.624) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.693, test=0.605) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.691, test=0.621) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.692, test=0.631) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.676, test=0.625) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.678, test=0.625) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.682, test=0.620) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.685, test=0.625) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.679, test=0.634) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.683, test=0.618) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.677, test=0.624) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.689, test=0.610) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.683, test=0.625) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.691, test=0.632) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.675, test=0.628) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.675, test=0.629) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.681, test=0.621) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.683, test=0.625) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.675, test=0.634) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.682, test=0.618) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.675, test=0.627) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.686, test=0.610) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.682, test=0.627) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.690, test=0.633) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.675, test=0.628) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.675, test=0.629) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.681, test=0.621) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.683, test=0.627) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.675, test=0.634) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.680, test=0.623) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.675, test=0.627) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.686, test=0.610) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.681, test=0.629) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.690, test=0.633) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.674, test=0.629) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.675, test=0.629) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.681, test=0.621) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.683, test=0.627) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.674, test=0.635) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.676, test=0.623) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.675, test=0.629) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.685, test=0.611) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.679, test=0.631) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.687, test=0.634) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.672, test=0.630) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.672, test=0.630) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.679, test=0.623) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.681, test=0.625) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.674, test=0.635) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.675, test=0.623) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.674, test=0.630) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.685, test=0.611) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.678, test=0.634) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.686, test=0.632) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.671, test=0.630) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.670, test=0.631) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.679, test=0.623) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.679, test=0.626) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.673, test=0.638) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.674, test=0.625) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.674, test=0.630) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.685, test=0.611) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.678, test=0.634) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.686, test=0.632) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.671, test=0.630) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.670, test=0.631) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.679, test=0.623) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.678, test=0.624) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.673, test=0.638) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.673, test=0.625) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.670, test=0.632) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.683, test=0.611) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.678, test=0.634) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.685, test=0.632) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.671, test=0.630) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.670, test=0.631) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.679, test=0.623) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.677, test=0.624) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.672, test=0.639) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.670, test=0.628) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.669, test=0.632) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.682, test=0.614) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.676, test=0.636) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.678, test=0.633) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.667, test=0.632) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.669, test=0.631) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.675, test=0.625) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.674, test=0.631) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.672, test=0.639) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.666, test=0.632) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.669, test=0.632) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.682, test=0.614) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.676, test=0.636) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.678, test=0.633) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.667, test=0.632) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.669, test=0.631) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.674, test=0.626) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.674, test=0.632) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.672, test=0.639) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.665, test=0.633) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.668, test=0.635) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.682, test=0.614) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.676, test=0.636) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.678, test=0.633) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.667, test=0.632) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.669, test=0.632) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.674, test=0.626) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.674, test=0.632) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.672, test=0.639) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.664, test=0.632) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.668, test=0.637) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.681, test=0.612) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.675, test=0.637) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.678, test=0.633) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.667, test=0.632) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.669, test=0.632) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.674, test=0.626) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.674, test=0.632) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.671, test=0.639) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.664, test=0.634) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.668, test=0.637) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.680, test=0.613) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.675, test=0.637) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.675, test=0.637) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.667, test=0.632) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.667, test=0.631) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.674, test=0.626) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.669, test=0.635) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.670, test=0.642) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.663, test=0.633) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.666, test=0.636) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.678, test=0.616) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.673, test=0.639) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.672, test=0.641) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.663, test=0.631) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.665, test=0.628) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.670, test=0.629) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.661, test=0.639) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.670, test=0.642) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.663, test=0.633) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.666, test=0.637) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.678, test=0.616) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.673, test=0.639) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.672, test=0.641) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.663, test=0.631) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.662, test=0.626) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.668, test=0.629) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.661, test=0.639) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.669, test=0.642) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.662, test=0.634) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.666, test=0.637) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.672, test=0.622) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.673, test=0.639) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.668, test=0.642) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.662, test=0.631) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.662, test=0.627) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.667, test=0.633) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.660, test=0.636) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.661, test=0.642) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.661, test=0.640) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.663, test=0.639) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.670, test=0.621) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.667, test=0.646) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.665, test=0.642) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.660, test=0.630) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.661, test=0.626) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.666, test=0.631) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.659, test=0.638) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.661, test=0.642) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.661, test=0.640) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.661, test=0.639) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.670, test=0.621) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.666, test=0.646) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.665, test=0.642) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.660, test=0.630) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.661, test=0.626) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.664, test=0.631) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.659, test=0.638) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.655, test=0.646) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.659, test=0.640) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.659, test=0.640) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.661, test=0.627) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.665, test=0.645) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.660, test=0.651) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.656, test=0.629) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.661, test=0.626) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.664, test=0.631) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.658, test=0.637) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.655, test=0.646) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.659, test=0.640) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.656, test=0.641) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.661, test=0.627) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.665, test=0.645) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.660, test=0.651) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.656, test=0.629) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.661, test=0.626) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.664, test=0.631) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.658, test=0.637) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.654, test=0.645) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.654, test=0.643) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.655, test=0.640) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.658, test=0.630) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.658, test=0.652) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.654, test=0.651) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.654, test=0.634) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.661, test=0.624) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.661, test=0.629) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.656, test=0.639) total time=   0.1s
[CV 1/10] END ccp_alpha=0.000286560417042489;, score=(train=0.652, test=0.644) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000286560417042489;, score=(train=0.647, test=0.636) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000286560417042489;, score=(train=0.652, test=0.642) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000286560417042489;, score=(train=0.653, test=0.624) total time=   0.1s
[CV 5/10] END ccp_alpha=0.000286560417042489;, score=(train=0.650, test=0.654) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000286560417042489;, score=(train=0.652, test=0.651) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000286560417042489;, score=(train=0.649, test=0.634) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000286560417042489;, score=(train=0.657, test=0.623) total time=   0.1s
[CV 9/10] END ccp_alpha=0.000286560417042489;, score=(train=0.657, test=0.625) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000286560417042489;, score=(train=0.652, test=0.638) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.649, test=0.645) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.644, test=0.634) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.651, test=0.642) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.650, test=0.624) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.649, test=0.653) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.652, test=0.651) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.649, test=0.634) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.650, test=0.615) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.654, test=0.625) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.651, test=0.638) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.646, test=0.645) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.641, test=0.638) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.649, test=0.644) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.649, test=0.625) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.647, test=0.653) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.649, test=0.654) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.648, test=0.632) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.648, test=0.617) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.650, test=0.624) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.650, test=0.636) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.646, test=0.645) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.641, test=0.638) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.649, test=0.644) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.649, test=0.625) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.647, test=0.653) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.649, test=0.654) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.648, test=0.632) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.648, test=0.617) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.650, test=0.624) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.650, test=0.636) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.646, test=0.645) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.641, test=0.639) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.638, test=0.631) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.648, test=0.624) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.640, test=0.645) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.645, test=0.655) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.644, test=0.633) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.643, test=0.611) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.646, test=0.635) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.643, test=0.632) total time=   0.1s
[CV 1/10] END ccp_alpha=0.000414271464091287;, score=(train=0.646, test=0.645) total time=   0.2s
[CV 2/10] END ccp_alpha=0.000414271464091287;, score=(train=0.640, test=0.638) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000414271464091287;, score=(train=0.638, test=0.631) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000414271464091287;, score=(train=0.648, test=0.624) total time=   0.2s
[CV 5/10] END ccp_alpha=0.000414271464091287;, score=(train=0.639, test=0.644) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000414271464091287;, score=(train=0.645, test=0.655) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000414271464091287;, score=(train=0.644, test=0.633) total time=   0.2s
[CV 8/10] END ccp_alpha=0.000414271464091287;, score=(train=0.643, test=0.611) total time=   0.2s
[CV 9/10] END ccp_alpha=0.000414271464091287;, score=(train=0.646, test=0.635) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000414271464091287;, score=(train=0.643, test=0.632) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.646, test=0.645) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.640, test=0.637) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.638, test=0.631) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.647, test=0.621) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.637, test=0.642) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.645, test=0.655) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.644, test=0.633) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.641, test=0.614) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.645, test=0.635) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.643, test=0.632) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.646, test=0.645) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.640, test=0.637) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.638, test=0.631) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.646, test=0.620) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.636, test=0.641) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.645, test=0.655) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.642, test=0.634) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.641, test=0.614) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.645, test=0.635) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.643, test=0.632) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.638, test=0.644) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.632, test=0.632) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.635, test=0.633) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.638, test=0.615) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.636, test=0.641) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.636, test=0.649) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.640, test=0.636) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.638, test=0.607) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.633, test=0.630) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.637, test=0.631) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000674509767592063;, score=(train=0.636, test=0.643) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000674509767592063;, score=(train=0.632, test=0.632) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000674509767592063;, score=(train=0.629, test=0.628) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000674509767592063;, score=(train=0.632, test=0.609) total time=   0.1s
[CV 5/10] END ccp_alpha=0.000674509767592063;, score=(train=0.636, test=0.641) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000674509767592063;, score=(train=0.629, test=0.643) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000674509767592063;, score=(train=0.637, test=0.634) total time=   0.2s
[CV 8/10] END ccp_alpha=0.000674509767592063;, score=(train=0.633, test=0.602) total time=   0.1s
[CV 9/10] END ccp_alpha=0.000674509767592063;, score=(train=0.630, test=0.628) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000674509767592063;, score=(train=0.637, test=0.631) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.626, test=0.633) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.630, test=0.633) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.625, test=0.624) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.632, test=0.609) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.625, test=0.639) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.629, test=0.643) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.637, test=0.634) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.633, test=0.602) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.630, test=0.628) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.627, test=0.625) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.626, test=0.633) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.616, test=0.621) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.624, test=0.623) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.628, test=0.607) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.625, test=0.639) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.615, test=0.621) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.626, test=0.633) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.629, test=0.599) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.626, test=0.625) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.626, test=0.624) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.625, test=0.632) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.615, test=0.620) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.624, test=0.623) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.618, test=0.595) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.614, test=0.628) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.615, test=0.621) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.615, test=0.619) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.629, test=0.599) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.626, test=0.625) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.626, test=0.624) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.602, test=0.610) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.602, test=0.606) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.614, test=0.616) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.605, test=0.582) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.614, test=0.628) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.602, test=0.608) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.603, test=0.602) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.618, test=0.591) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.615, test=0.619) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.603, test=0.597) total time=   0.2s
[CV 1/10] END ccp_alpha=0.003213844189509124;, score=(train=0.594, test=0.603) total time=   0.2s
[CV 2/10] END ccp_alpha=0.003213844189509124;, score=(train=0.595, test=0.598) total time=   0.1s
[CV 3/10] END ccp_alpha=0.003213844189509124;, score=(train=0.600, test=0.606) total time=   0.2s
[CV 4/10] END ccp_alpha=0.003213844189509124;, score=(train=0.597, test=0.573) total time=   0.1s
[CV 5/10] END ccp_alpha=0.003213844189509124;, score=(train=0.601, test=0.617) total time=   0.2s
[CV 6/10] END ccp_alpha=0.003213844189509124;, score=(train=0.595, test=0.595) total time=   0.1s
[CV 7/10] END ccp_alpha=0.003213844189509124;, score=(train=0.603, test=0.602) total time=   0.1s
[CV 8/10] END ccp_alpha=0.003213844189509124;, score=(train=0.604, test=0.588) total time=   0.1s
[CV 9/10] END ccp_alpha=0.003213844189509124;, score=(train=0.602, test=0.609) total time=   0.2s
[CV 10/10] END ccp_alpha=0.003213844189509124;, score=(train=0.596, test=0.588) total time=   0.1s
[CV 1/10] END ccp_alpha=0.020034588772265882;, score=(train=0.500, test=0.500) total time=   0.2s
[CV 2/10] END ccp_alpha=0.020034588772265882;, score=(train=0.500, test=0.500) total time=   0.1s
[CV 3/10] END ccp_alpha=0.020034588772265882;, score=(train=0.500, test=0.500) total time=   0.1s
[CV 4/10] END ccp_alpha=0.020034588772265882;, score=(train=0.597, test=0.573) total time=   0.1s
[CV 5/10] END ccp_alpha=0.020034588772265882;, score=(train=0.500, test=0.500) total time=   0.2s
[CV 6/10] END ccp_alpha=0.020034588772265882;, score=(train=0.500, test=0.500) total time=   0.1s
[CV 7/10] END ccp_alpha=0.020034588772265882;, score=(train=0.500, test=0.500) total time=   0.2s
[CV 8/10] END ccp_alpha=0.020034588772265882;, score=(train=0.596, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.020034588772265882;, score=(train=0.500, test=0.500) total time=   0.2s
[CV 10/10] END ccp_alpha=0.020034588772265882;, score=(train=0.596, test=0.588) total time=   0.2s
Out[66]:
GridSearchCV(cv=KFold(n_splits=10, random_state=42, shuffle=True),
             estimator=DecisionTreeClassifier(random_state=42),
             param_grid={'ccp_alpha': array([0.00000000e+00, 2.66666667e-05, 2.85714286e-05, ...,
       2.61221640e-03, 3.21384419e-03, 2.00345888e-02])},
             return_train_score=True, scoring='roc_auc', verbose=4)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GridSearchCV(cv=KFold(n_splits=10, random_state=42, shuffle=True),
             estimator=DecisionTreeClassifier(random_state=42),
             param_grid={'ccp_alpha': array([0.00000000e+00, 2.66666667e-05, 2.85714286e-05, ...,
       2.61221640e-03, 3.21384419e-03, 2.00345888e-02])},
             return_train_score=True, scoring='roc_auc', verbose=4)
DecisionTreeClassifier(ccp_alpha=0.0002577008335828551, random_state=42)
DecisionTreeClassifier(ccp_alpha=0.0002577008335828551, random_state=42)

Model Performance¶

In [68]:
report_GridSearchCV_results(grid_search_post_prune)
- Best combination of hyperparams:
 {'ccp_alpha': 0.0002577008335828551} 

- Best mean_test_score:
 0.6385992855021885 

- Score by fold for best estimator:
 [0.6445609038774083, 0.6429567488600647, 0.64027184707424, 0.6303182836899942, 0.6515900666379598, 0.6506916876817452, 0.6337261049457951, 0.6242858044524325, 0.6286588306845862, 0.6389325771176594] 

- Top 10 hyperparameter combinations by mean_test_score:
mean_test_score param_ccp_alpha
rank_test_score
1 0.638599 0.000258
2 0.637341 0.000241
3 0.637245 0.000240
4 0.637043 0.000287
5 0.636850 0.000333
5 0.636850 0.000333
7 0.636076 0.000306
8 0.635346 0.000233
9 0.635314 0.000231
10 0.634850 0.000395
In [69]:
compare_performance(grid_search_post_prune)
Out[69]:
train_AUC val_AUC
1 1.0 0.533032
2 1.0 0.533008
3 1.0 0.533080
4 1.0 0.533080
5 1.0 0.533080
6 1.0 0.533079
7 1.0 0.533079
8 1.0 0.533109
9 1.0 0.533109
10 1.0 0.533109
Mean 1.0 0.533077
In [70]:
best_model_post_prune=grid_search_post_prune.best_estimator_
In [71]:
plot_probability_std(best_model_post_prune, df_train, y_name, x_name, kf10, "Post-pruned Classification Tree")
No description has been provided for this image
In [72]:
plot_avg_feature_importance(best_model_post_prune, df_train, y_name, x_name, kf10, "Post-pruned Classification Tree")
No description has been provided for this image
In [73]:
evaluate_model(best_model_post_prune, df_X_test_scaled, df_y_test)
Test AUC: 0.64
Accuracy: 0.60
Confusion Matrix:
[[1843  815]
 [1179 1163]]
No description has been provided for this image
Classification Report:
              precision    recall  f1-score   support

           0       0.61      0.69      0.65      2658
           1       0.59      0.50      0.54      2342

    accuracy                           0.60      5000
   macro avg       0.60      0.59      0.59      5000
weighted avg       0.60      0.60      0.60      5000

In [74]:
plot_roc_curve(best_model_post_prune, df_X_test_scaled, df_y_test)
No description has been provided for this image
In [75]:
# Plotting the tree
plt.figure(figsize=(50, 20))
plot_tree(best_model_post_prune, filled=True, feature_names=X_train.columns, class_names=['Not readmitted', 'Readmitted'], rounded=True)
plt.show()
No description has been provided for this image

Random Forest Model (with regularization)¶

In [84]:
# Initialize model
randomforest = RandomForestClassifier(max_depth = 6, random_state = 42, bootstrap=True)

# Define the hyperparameter grid
rf_param_grid = {
    'max_depth': [2, 3, 4],
    'min_samples_leaf': [500, 1000, 2000],
    'max_features': [2, 3], 
}

# Create a GridSearchCV object
grid_search_rf = GridSearchCV(estimator=randomforest, param_grid=rf_param_grid, cv=kf10, scoring='roc_auc', verbose=4, return_train_score=True)

# Fit the GridSearchCV object to the training data
# To resolve error: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of 
# y to (n_samples,), for example using ravel().return fit_method(estimator, *args, **kwargs)
grid_search_rf.fit(df_train[x_name], df_train[y_name].values.ravel())
Fitting 10 folds for each of 18 candidates, totalling 180 fits
[CV 1/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.639, test=0.639) total time=   0.3s
[CV 2/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.640, test=0.647) total time=   0.3s
[CV 3/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.640, test=0.639) total time=   0.3s
[CV 4/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.642, test=0.615) total time=   0.4s
[CV 5/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.639, test=0.643) total time=   0.5s
[CV 6/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.639, test=0.649) total time=   0.4s
[CV 7/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.641, test=0.635) total time=   0.4s
[CV 8/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.642, test=0.612) total time=   0.3s
[CV 9/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.640, test=0.628) total time=   0.3s
[CV 10/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.641, test=0.639) total time=   0.3s
[CV 1/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.639) total time=   0.3s
[CV 2/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.637, test=0.647) total time=   0.3s
[CV 3/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.640) total time=   0.3s
[CV 4/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.642, test=0.615) total time=   0.3s
[CV 5/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.643) total time=   0.4s
[CV 6/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.649) total time=   0.3s
[CV 7/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.634) total time=   0.3s
[CV 8/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.641, test=0.612) total time=   0.3s
[CV 9/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.630) total time=   0.3s
[CV 10/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.640, test=0.636) total time=   0.3s
[CV 1/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.623, test=0.617) total time=   0.3s
[CV 2/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.623, test=0.638) total time=   0.3s
[CV 3/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.622, test=0.625) total time=   0.4s
[CV 4/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.627, test=0.600) total time=   0.3s
[CV 5/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.624, test=0.628) total time=   0.3s
[CV 6/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.622, test=0.635) total time=   0.4s
[CV 7/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.625, test=0.614) total time=   0.3s
[CV 8/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.625, test=0.610) total time=   0.5s
[CV 9/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.623, test=0.624) total time=   0.3s
[CV 10/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.626, test=0.613) total time=   0.3s
[CV 1/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.648) total time=   0.3s
[CV 2/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.657) total time=   0.5s
[CV 3/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.644, test=0.645) total time=   0.5s
[CV 4/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.649, test=0.618) total time=   0.4s
[CV 5/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.654) total time=   0.4s
[CV 6/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.652) total time=   0.4s
[CV 7/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.643) total time=   0.4s
[CV 8/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.648, test=0.616) total time=   0.4s
[CV 9/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.629) total time=   0.4s
[CV 10/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.647) total time=   0.4s
[CV 1/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.646) total time=   0.4s
[CV 2/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.656) total time=   0.4s
[CV 3/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.641, test=0.643) total time=   0.4s
[CV 4/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.646, test=0.615) total time=   0.3s
[CV 5/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.642, test=0.651) total time=   0.4s
[CV 6/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.649) total time=   0.4s
[CV 7/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.644, test=0.642) total time=   0.4s
[CV 8/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.645, test=0.614) total time=   0.4s
[CV 9/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.642, test=0.626) total time=   0.4s
[CV 10/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.644) total time=   0.4s
[CV 1/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.624, test=0.622) total time=   0.4s
[CV 2/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.625, test=0.639) total time=   0.4s
[CV 3/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.623, test=0.623) total time=   0.4s
[CV 4/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.629, test=0.601) total time=   0.3s
[CV 5/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.625, test=0.630) total time=   0.4s
[CV 6/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.622, test=0.632) total time=   0.4s
[CV 7/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.625, test=0.622) total time=   0.4s
[CV 8/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.627, test=0.611) total time=   0.4s
[CV 9/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.625, test=0.618) total time=   0.4s
[CV 10/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.626, test=0.615) total time=   0.4s
[CV 1/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.642, test=0.642) total time=   0.4s
[CV 2/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.645, test=0.651) total time=   0.4s
[CV 3/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.643, test=0.644) total time=   0.4s
[CV 4/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.647, test=0.615) total time=   0.3s
[CV 5/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.642, test=0.648) total time=   0.4s
[CV 6/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.643, test=0.649) total time=   0.4s
[CV 7/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.644, test=0.638) total time=   0.4s
[CV 8/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.644, test=0.616) total time=   0.4s
[CV 9/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.644, test=0.633) total time=   0.4s
[CV 10/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.644, test=0.642) total time=   0.4s
[CV 1/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.639) total time=   0.4s
[CV 2/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.647) total time=   0.3s
[CV 3/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.641) total time=   0.4s
[CV 4/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.642, test=0.614) total time=   0.4s
[CV 5/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.637, test=0.643) total time=   0.3s
[CV 6/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.647) total time=   0.3s
[CV 7/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.634) total time=   0.3s
[CV 8/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.641, test=0.613) total time=   0.5s
[CV 9/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.640, test=0.631) total time=   0.4s
[CV 10/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.634) total time=   0.3s
[CV 1/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.622, test=0.615) total time=   0.3s
[CV 2/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.623, test=0.638) total time=   0.3s
[CV 3/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.621, test=0.623) total time=   0.4s
[CV 4/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.627, test=0.599) total time=   0.3s
[CV 5/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.623, test=0.628) total time=   0.4s
[CV 6/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.621, test=0.633) total time=   0.3s
[CV 7/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.625, test=0.613) total time=   0.3s
[CV 8/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.624, test=0.609) total time=   0.3s
[CV 9/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.623, test=0.624) total time=   0.4s
[CV 10/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.625, test=0.613) total time=   0.5s
[CV 1/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.647) total time=   0.6s
[CV 2/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.653) total time=   0.5s
[CV 3/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.647) total time=   0.6s
[CV 4/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.650, test=0.615) total time=   0.4s
[CV 5/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.654) total time=   0.6s
[CV 6/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.649) total time=   0.4s
[CV 7/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.640) total time=   0.5s
[CV 8/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.649, test=0.618) total time=   0.4s
[CV 9/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.630) total time=   0.5s
[CV 10/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.651) total time=   0.4s
[CV 1/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.641, test=0.642) total time=   0.3s
[CV 2/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.641, test=0.652) total time=   0.4s
[CV 3/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.641, test=0.644) total time=   0.5s
[CV 4/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.646, test=0.614) total time=   0.4s
[CV 5/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.641, test=0.647) total time=   0.4s
[CV 6/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.648) total time=   0.4s
[CV 7/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.639) total time=   0.4s
[CV 8/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.645, test=0.617) total time=   0.4s
[CV 9/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.641, test=0.626) total time=   0.3s
[CV 10/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.642, test=0.644) total time=   0.4s
[CV 1/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.627, test=0.625) total time=   0.4s
[CV 2/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.626, test=0.640) total time=   0.4s
[CV 3/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.624, test=0.626) total time=   0.4s
[CV 4/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.631, test=0.600) total time=   0.4s
[CV 5/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.626, test=0.631) total time=   0.4s
[CV 6/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.623, test=0.633) total time=   0.3s
[CV 7/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.627, test=0.624) total time=   0.3s
[CV 8/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.628, test=0.614) total time=   0.3s
[CV 9/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.626, test=0.619) total time=   0.3s
[CV 10/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.628, test=0.617) total time=   0.4s
[CV 1/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.648) total time=   0.4s
[CV 2/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.650) total time=   0.4s
[CV 3/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.645, test=0.644) total time=   0.3s
[CV 4/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.648, test=0.613) total time=   0.4s
[CV 5/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.651) total time=   0.4s
[CV 6/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.644, test=0.650) total time=   0.5s
[CV 7/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.641) total time=   0.5s
[CV 8/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.647, test=0.615) total time=   0.3s
[CV 9/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.634) total time=   0.4s
[CV 10/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.644) total time=   0.4s
[CV 1/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.639) total time=   0.4s
[CV 2/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.648) total time=   0.3s
[CV 3/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.640, test=0.643) total time=   0.4s
[CV 4/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.642, test=0.611) total time=   0.3s
[CV 5/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.643) total time=   0.3s
[CV 6/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.647) total time=   0.4s
[CV 7/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.633) total time=   0.4s
[CV 8/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.642, test=0.611) total time=   0.4s
[CV 9/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.640, test=0.631) total time=   0.4s
[CV 10/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.635) total time=   0.3s
[CV 1/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.622, test=0.615) total time=   0.3s
[CV 2/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.623, test=0.638) total time=   0.3s
[CV 3/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.621, test=0.623) total time=   0.3s
[CV 4/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.627, test=0.599) total time=   0.3s
[CV 5/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.623, test=0.628) total time=   0.4s
[CV 6/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.621, test=0.633) total time=   0.3s
[CV 7/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.625, test=0.613) total time=   0.3s
[CV 8/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.624, test=0.609) total time=   0.3s
[CV 9/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.623, test=0.624) total time=   0.3s
[CV 10/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.625, test=0.613) total time=   0.3s
[CV 1/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.647, test=0.648) total time=   0.4s
[CV 2/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.651) total time=   0.5s
[CV 3/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.648) total time=   0.4s
[CV 4/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.651, test=0.616) total time=   0.4s
[CV 5/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.655) total time=   0.5s
[CV 6/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.647, test=0.651) total time=   0.4s
[CV 7/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.639) total time=   0.4s
[CV 8/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.651, test=0.620) total time=   0.6s
[CV 9/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.647, test=0.631) total time=   0.5s
[CV 10/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.647, test=0.650) total time=   0.5s
[CV 1/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.643) total time=   0.4s
[CV 2/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.642, test=0.653) total time=   0.5s
[CV 3/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.642, test=0.647) total time=   0.4s
[CV 4/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.646, test=0.615) total time=   0.5s
[CV 5/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.642, test=0.648) total time=   0.4s
[CV 6/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.644, test=0.649) total time=   0.5s
[CV 7/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.644, test=0.639) total time=   0.4s
[CV 8/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.646, test=0.620) total time=   0.4s
[CV 9/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.628) total time=   0.4s
[CV 10/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.642) total time=   0.4s
[CV 1/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.627, test=0.625) total time=   0.3s
[CV 2/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.626, test=0.640) total time=   0.4s
[CV 3/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.624, test=0.626) total time=   0.4s
[CV 4/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.631, test=0.600) total time=   0.4s
[CV 5/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.626, test=0.631) total time=   0.4s
[CV 6/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.623, test=0.633) total time=   0.4s
[CV 7/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.627, test=0.624) total time=   0.4s
[CV 8/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.628, test=0.614) total time=   0.4s
[CV 9/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.626, test=0.619) total time=   0.3s
[CV 10/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.628, test=0.617) total time=   0.3s
Out[84]:
GridSearchCV(cv=KFold(n_splits=10, random_state=42, shuffle=True),
             estimator=RandomForestClassifier(max_depth=6, random_state=42),
             param_grid={'max_depth': [2, 3, 4], 'max_features': [2, 3],
                         'min_samples_leaf': [500, 1000, 2000]},
             return_train_score=True, scoring='roc_auc', verbose=4)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GridSearchCV(cv=KFold(n_splits=10, random_state=42, shuffle=True),
             estimator=RandomForestClassifier(max_depth=6, random_state=42),
             param_grid={'max_depth': [2, 3, 4], 'max_features': [2, 3],
                         'min_samples_leaf': [500, 1000, 2000]},
             return_train_score=True, scoring='roc_auc', verbose=4)
RandomForestClassifier(max_depth=4, max_features=3, min_samples_leaf=500,
                       random_state=42)
RandomForestClassifier(max_depth=4, max_features=3, min_samples_leaf=500,
                       random_state=42)

Model Performance¶

In [87]:
report_GridSearchCV_results(grid_search_rf)
- Best combination of hyperparams:
 {'max_depth': 4, 'max_features': 3, 'min_samples_leaf': 500} 

- Best mean_test_score:
 0.6409978359812152 

- Score by fold for best estimator:
 [0.6482674781776252, 0.6513156041355608, 0.6480194987161607, 0.6160162023648865, 0.6545420509716655, 0.6511617618076586, 0.6392177577137725, 0.619981345507844, 0.6313241923611761, 0.650132468055802] 

- Top 10 hyperparameter combinations by mean_test_score:
mean_test_score param_max_depth param_min_samples_leaf param_max_features
rank_test_score
1 0.640998 4 500 3
2 0.640955 2 500 3
3 0.640234 3 500 3
4 0.639044 4 500 2
5 0.638614 2 1000 3
6 0.638317 4 1000 3
7 0.637706 3 500 2
8 0.637399 3 1000 3
9 0.634454 2 500 2
10 0.634365 2 1000 2
In [89]:
compare_performance(grid_search_rf)
Out[89]:
train_AUC val_AUC
1 0.640278 0.634454
2 0.639069 0.634365
3 0.623916 0.620221
4 0.646015 0.640955
5 0.643191 0.638614
6 0.625180 0.621237
7 0.643744 0.637706
8 0.639416 0.634266
9 0.623525 0.619434
10 0.646405 0.640234
Mean 0.637074 0.632149
In [91]:
best_model_rf=grid_search_rf.best_estimator_
In [95]:
plot_probability_std(best_model_rf, df_train, y_name, x_name, kf10, "Bagged Random Forest")
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
No description has been provided for this image
In [99]:
plot_avg_feature_importance(best_model_rf, df_train, y_name, x_name, kf10, "Pre-pruned Classification Tree")
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
No description has been provided for this image
In [101]:
evaluate_model(best_model_rf, df_X_test_scaled, df_y_test)
Test AUC: 0.64
Accuracy: 0.59
Confusion Matrix:
[[2344  314]
 [1747  595]]
No description has been provided for this image
Classification Report:
              precision    recall  f1-score   support

           0       0.57      0.88      0.69      2658
           1       0.65      0.25      0.37      2342

    accuracy                           0.59      5000
   macro avg       0.61      0.57      0.53      5000
weighted avg       0.61      0.59      0.54      5000

In [103]:
plot_roc_curve(best_model_rf, df_X_test_scaled, df_y_test)
No description has been provided for this image

XGBoost (With regularization)¶

In [108]:
# Initialize model
xgb_model = xgb.XGBClassifier(random_state = 42)

# Define the hyperparameter grid
xgb_param_grid = {
    'colsample_bytree': [0.3, 0.7],
    'n_estimators': [50, 100, 200],
    'max_depth': [2, 5, 10],
    'alpha': [0, 0.1, 1], # Alpha/lasso regularisation
    'lambda': [0, 0.1, 1], # Lambda/ridge regularisation
    'learning_rate': [0.01, 0.05]    
}

# Create a GridSearchCV object
grid_search_xgb = GridSearchCV(param_grid=xgb_param_grid, estimator=xgb_model, 
                        scoring='roc_auc', cv=kf10, verbose=4, return_train_score=True)

# Fit the GridSearchCV object to the training data
grid_search_xgb.fit(df_train[x_name], df_train[y_name])
Fitting 10 folds for each of 324 candidates, totalling 3240 fits
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.656) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.654) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.648) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.621) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.653) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.660) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.623) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.644) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.655) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.654) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.621) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.662) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.644) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.623) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.642) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.646) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.624) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.626) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.661) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.660) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.662) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.628) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.661) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.664) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.654) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.637) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.645) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.653) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.661) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.666) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.631) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.662) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.665) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.639) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.654) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.664) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.662) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.668) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.636) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.662) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.654) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.641) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.646) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.657) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.655) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.659) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.660) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.621) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.660) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.655) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.651) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.643) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.636) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.809, test=0.655) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.659) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.829, test=0.660) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.662) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.625) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.659) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.829, test=0.658) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.832, test=0.656) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.829, test=0.645) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.829, test=0.636) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.826, test=0.657) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.853, test=0.663) total time=   0.7s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.851, test=0.662) total time=   0.6s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.664) total time=   0.6s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.853, test=0.628) total time=   0.6s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.850, test=0.659) total time=   0.6s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.851, test=0.658) total time=   0.8s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.854, test=0.656) total time=   1.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.850, test=0.646) total time=   0.8s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.853, test=0.642) total time=   0.8s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.659) total time=   0.8s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.658) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.654) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.624) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.656) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.625) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.654) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.649) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.665) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.655) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.662) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.658) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.653) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.648) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.659) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.664) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.630) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.660) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.656) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.638) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.644) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.654) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.662) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.666) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.634) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.663) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.661) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.640) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.656) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.729, test=0.662) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.663) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.667) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.639) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.663) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.661) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.660) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.644) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.650) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.659) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.648) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.657) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.846, test=0.656) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.613) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.661) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.653) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.856, test=0.643) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.644) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.857, test=0.632) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.649) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.894, test=0.652) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.892, test=0.653) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.655) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.896, test=0.614) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.662) total time=   0.6s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.651) total time=   0.6s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.645) total time=   0.6s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.894, test=0.646) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.632) total time=   0.6s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.892, test=0.652) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.941, test=0.646) total time=   1.0s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.939, test=0.648) total time=   0.9s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.650) total time=   0.9s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.943, test=0.610) total time=   0.7s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.947, test=0.656) total time=   0.8s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.944, test=0.645) total time=   0.8s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.947, test=0.641) total time=   0.8s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.940, test=0.643) total time=   0.9s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.940, test=0.632) total time=   0.8s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.650) total time=   0.8s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.655) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.654) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.648) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.621) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.653) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.660) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.623) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.645) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.654) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.621) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.644) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.623) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.624) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.662) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.644) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.661) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.660) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.662) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.629) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.661) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.664) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.637) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.652) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.661) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.666) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.631) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.662) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.665) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.639) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.654) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.664) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.662) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.668) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.636) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.661) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.667) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.655) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.641) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.646) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.657) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.811, test=0.655) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.660) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.808, test=0.660) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.621) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.811, test=0.661) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.811, test=0.655) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.651) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.644) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.635) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.807, test=0.655) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.829, test=0.660) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.661) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.825, test=0.662) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.829, test=0.624) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.826, test=0.660) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.658) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.655) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.645) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.635) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.823, test=0.656) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.851, test=0.663) total time=   0.8s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.662) total time=   0.9s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.664) total time=   1.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.851, test=0.627) total time=   0.8s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.660) total time=   0.8s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.659) total time=   0.8s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.851, test=0.656) total time=   0.8s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.645) total time=   1.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.850, test=0.641) total time=   0.9s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.659) total time=   0.9s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.658) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.654) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.624) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.656) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.661) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.625) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.654) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.649) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.643) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.664) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.655) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.663) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.633) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.658) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.666) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.652) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.649) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.651) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.659) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.665) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.630) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.660) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.656) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.638) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.654) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.663) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.665) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.633) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.663) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.662) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.661) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.640) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.645) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.656) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.663) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.662) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.666) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.729, test=0.638) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.660) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.661) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.661) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.645) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.651) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.660) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.652) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.654) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.844, test=0.655) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.616) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.660) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.851, test=0.653) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.642) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.646) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.632) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.652) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.892, test=0.654) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.652) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.886, test=0.654) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.894, test=0.616) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.895, test=0.659) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.894, test=0.654) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.895, test=0.644) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.648) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.893, test=0.635) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.890, test=0.656) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.939, test=0.648) total time=   0.9s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.936, test=0.649) total time=   0.9s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.935, test=0.649) total time=   0.8s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.940, test=0.613) total time=   0.8s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.946, test=0.654) total time=   0.8s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.940, test=0.648) total time=   0.8s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.946, test=0.640) total time=   1.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.939, test=0.643) total time=   0.8s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.632) total time=   0.8s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.653) total time=   0.8s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.655) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.621) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.652) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.660) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.623) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.644) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.654) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.621) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.661) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.644) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.623) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.655) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.624) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.662) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.626) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.644) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.659) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.676, test=0.661) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.628) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.661) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.638) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.652) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.661) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.680, test=0.666) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.631) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.662) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.665) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.653) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.639) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.643) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.654) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.664) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.662) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.687, test=0.668) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.635) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.662) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.666) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.654) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.641) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.646) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.657) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.795, test=0.657) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.797, test=0.658) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.793, test=0.659) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.796, test=0.623) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.795, test=0.662) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.796, test=0.654) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.799, test=0.654) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.797, test=0.646) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.796, test=0.639) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.791, test=0.656) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.811, test=0.661) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.810, test=0.659) total time=   0.5s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.663) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.811, test=0.625) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.809, test=0.660) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.810, test=0.658) total time=   0.5s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.813, test=0.656) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.810, test=0.646) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.810, test=0.637) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.805, test=0.658) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.831, test=0.664) total time=   1.0s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.830, test=0.661) total time=   0.9s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.828, test=0.664) total time=   1.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.831, test=0.630) total time=   0.9s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.830, test=0.660) total time=   1.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.830, test=0.660) total time=   1.0s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.833, test=0.657) total time=   1.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.829, test=0.647) total time=   1.4s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.831, test=0.643) total time=   1.4s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.827, test=0.659) total time=   1.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.658) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.654) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.624) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.656) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.661) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.625) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.663) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.649) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.643) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.663) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.656) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.663) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.659) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.666) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.652) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.633) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.649) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.658) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.663) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.630) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.661) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.661) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.639) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.655) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.662) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.699, test=0.665) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.633) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.664) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.659) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.642) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.646) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.657) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.723, test=0.663) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.661) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.666) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.722, test=0.638) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.718, test=0.662) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.718, test=0.661) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.660) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.647) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.650) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.658) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.660) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.658) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.657) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.834, test=0.620) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.658) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.833, test=0.653) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.836, test=0.652) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.646) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.835, test=0.639) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.653) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.871, test=0.656) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.869, test=0.656) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.866, test=0.654) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.873, test=0.624) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.874, test=0.659) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.873, test=0.656) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.876, test=0.648) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.869, test=0.648) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.873, test=0.637) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.869, test=0.656) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.916, test=0.654) total time=   0.9s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.914, test=0.648) total time=   0.9s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.915, test=0.650) total time=   0.8s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.918, test=0.621) total time=   0.8s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.922, test=0.655) total time=   0.8s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.922, test=0.653) total time=   0.9s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.926, test=0.645) total time=   0.9s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.917, test=0.649) total time=   0.8s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.918, test=0.637) total time=   0.8s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.915, test=0.654) total time=   0.8s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.651) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.651) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.646) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.619) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.648) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.657) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.620) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.639) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.638) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.652) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.644) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.618) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.653) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.660) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.642) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.620) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.641) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.654) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.647) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.622) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.654) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.657) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.645) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.649, test=0.620) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.641) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.641) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.651) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.666, test=0.660) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.626) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.655) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.658) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.647) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.631) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.639) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.647) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.662) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.627) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.657) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.660) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.649) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.632) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.640) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.649) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.661) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.656) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.665) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.629) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.660) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.663) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.650) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.634) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.642) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.651) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.658) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.654) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.809, test=0.652) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.823, test=0.611) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.651) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.819, test=0.651) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.639) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.633) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.631) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.656) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.832, test=0.659) total time=   0.6s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.832, test=0.651) total time=   0.5s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.653) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.616) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.651) total time=   0.5s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.835, test=0.651) total time=   0.6s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.642) total time=   0.6s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.835, test=0.636) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.835, test=0.631) total time=   0.6s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.655) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.661) total time=   1.0s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.652) total time=   1.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.857, test=0.656) total time=   1.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.618) total time=   1.0s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.861, test=0.648) total time=   1.0s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.863, test=0.650) total time=   1.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.643) total time=   1.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.856, test=0.637) total time=   1.0s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.631) total time=   1.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.857, test=0.654) total time=   1.0s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.654) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.647) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.624) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.651) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.657) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.643) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.620) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.640) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.653) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.655) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.664) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.648) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.626) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.664) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.656) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.663) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.633) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.659) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.652) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.635) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.649) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.651) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.659) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.663) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.630) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.661) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.648) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.637) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.641) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.650) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.661) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.661) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.666) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.634) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.660) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.651) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.644) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.644) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.651) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.661) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.663) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.666) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.634) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.658) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.663) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.651) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.651) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.645) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.742, test=0.654) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.653) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.868, test=0.655) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.864, test=0.646) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.612) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.650) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.650) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.872, test=0.643) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.865, test=0.637) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.631) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.863, test=0.649) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.918, test=0.648) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.915, test=0.653) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.914, test=0.648) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.911, test=0.609) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.913, test=0.645) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.918, test=0.646) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.921, test=0.641) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.910, test=0.638) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.917, test=0.629) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.910, test=0.652) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.959, test=0.645) total time=   0.8s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.961, test=0.644) total time=   0.7s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.961, test=0.648) total time=   0.7s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.959, test=0.603) total time=   0.8s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.961, test=0.639) total time=   0.8s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.962, test=0.638) total time=   0.8s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.965, test=0.638) total time=   0.7s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.962, test=0.640) total time=   0.7s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.961, test=0.627) total time=   0.9s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.957, test=0.650) total time=   0.8s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.651) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.651) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.646) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.619) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.648) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.657) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.620) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.639) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.638) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.652) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.644) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.618) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.653) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.660) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.642) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.620) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.641) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.654) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.647) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.622) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.654) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.657) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.645) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.649, test=0.620) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.641) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.641) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.651) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.666, test=0.661) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.626) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.655) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.658) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.647) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.632) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.639) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.662) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.627) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.658) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.660) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.649) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.632) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.649) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.661) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.656) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.665) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.629) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.660) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.663) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.649) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.635) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.642) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.651) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.659) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.653) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.808, test=0.650) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.822, test=0.614) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.817, test=0.652) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.651) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.820, test=0.639) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.634) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.633) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.655) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.661) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.651) total time=   0.6s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.826, test=0.652) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.839, test=0.616) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.834, test=0.652) total time=   0.5s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.833, test=0.651) total time=   0.5s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.641) total time=   0.6s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.833, test=0.636) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.833, test=0.631) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.654) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.857, test=0.662) total time=   1.0s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.857, test=0.651) total time=   1.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.854, test=0.655) total time=   1.0s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.618) total time=   1.0s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.648) total time=   0.9s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.860, test=0.650) total time=   1.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.863, test=0.641) total time=   1.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.854, test=0.637) total time=   1.0s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.860, test=0.631) total time=   1.0s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.855, test=0.653) total time=   1.0s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.654) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.647) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.624) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.650) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.657) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.644) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.620) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.640) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.652) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.655) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.664) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.648) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.627) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.662) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.656) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.664) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.633) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.660) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.652) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.634) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.649) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.651) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.660) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.663) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.630) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.661) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.650) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.636) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.641) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.649) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.662) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.661) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.668) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.632) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.659) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.667) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.653) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.641) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.643) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.652) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.664) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.662) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.669) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.633) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.660) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.663) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.655) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.646) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.644) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.655) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.655) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.868, test=0.646) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.864, test=0.650) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.615) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.868, test=0.648) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.648) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.873, test=0.643) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.861, test=0.636) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.867, test=0.633) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.860, test=0.651) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.913, test=0.650) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.915, test=0.641) total time=   0.5s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.647) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.909, test=0.612) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.906, test=0.643) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.914, test=0.641) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.918, test=0.640) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.904, test=0.636) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.914, test=0.634) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.904, test=0.652) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.957, test=0.645) total time=   0.8s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.964, test=0.638) total time=   0.8s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.960, test=0.643) total time=   0.9s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.959, test=0.606) total time=   0.8s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.962, test=0.639) total time=   1.0s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.957, test=0.636) total time=   0.8s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.962, test=0.638) total time=   0.8s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.959, test=0.632) total time=   0.8s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.960, test=0.628) total time=   0.8s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.649) total time=   0.9s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.651) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.651) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.646) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.619) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.648) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.657) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.620) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.639) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.638) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.652) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.644) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.618) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.653) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.660) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.642) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.620) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.641) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.654) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.647) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.622) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.654) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.657) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.644) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.619) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.641) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.641) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.658) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.650) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.666, test=0.660) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.626) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.655) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.659) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.647) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.631) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.651) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.671, test=0.662) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.627) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.658) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.660) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.648) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.632) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.640) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.648) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.679, test=0.661) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.655) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.679, test=0.665) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.628) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.660) total time=   0.5s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.663) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.649) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.635) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.641) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.650) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.803, test=0.662) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.655) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.799, test=0.654) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.614) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.806, test=0.654) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.808, test=0.656) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.639) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.807, test=0.633) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.806, test=0.636) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.805, test=0.653) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.817, test=0.662) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.653) total time=   0.6s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.816, test=0.655) total time=   0.6s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.618) total time=   0.6s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.822, test=0.653) total time=   0.6s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.822, test=0.656) total time=   0.6s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.824, test=0.642) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.636) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.821, test=0.635) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.654) total time=   0.6s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.842, test=0.663) total time=   1.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.842, test=0.653) total time=   1.4s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.839, test=0.659) total time=   1.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.620) total time=   1.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.652) total time=   1.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.654) total time=   1.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.642) total time=   1.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.839, test=0.637) total time=   1.0s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.633) total time=   1.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.841, test=0.654) total time=   1.0s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.654) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.648) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.649) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.624) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.652) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.657) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.644) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.620) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.658) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.652) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.627) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.664) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.647) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.626) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.663) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.655) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.663) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.632) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.660) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.652) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.634) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.649) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.649) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.656) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.663) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.631) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.662) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.650) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.636) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.649) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.662) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.661) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.667) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.633) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.662) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.665) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.654) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.642) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.653) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.664) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.664) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.669) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.634) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.660) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.663) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.654) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.644) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.647) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.657) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.855, test=0.657) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.650) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.847, test=0.650) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.619) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.855, test=0.649) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.855, test=0.647) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.858, test=0.650) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.845, test=0.639) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.635) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.654) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.898, test=0.653) total time=   0.7s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.890, test=0.651) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.885, test=0.651) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.888, test=0.619) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.890, test=0.651) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.887, test=0.643) total time=   0.5s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.650) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.879, test=0.642) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.890, test=0.636) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.887, test=0.655) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.937, test=0.647) total time=   0.8s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.940, test=0.648) total time=   0.8s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.932, test=0.647) total time=   0.7s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.937, test=0.616) total time=   0.8s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.937, test=0.645) total time=   0.7s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.933, test=0.640) total time=   0.8s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.945, test=0.646) total time=   0.9s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.640) total time=   0.7s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.936, test=0.633) total time=   0.8s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.931, test=0.655) total time=   0.7s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.655) total time=   0.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.654) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.648) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.621) total time=   0.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.653) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.660) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.623) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.644) total time=   0.0s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.654) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.621) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.644) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.623) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.654) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.624) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.662) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.644) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.661) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.660) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.662) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.628) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.662) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.664) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.637) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.652) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.661) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.666) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.631) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.662) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.665) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.654) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.639) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.643) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.654) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.664) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.662) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.668) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.635) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.662) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.666) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.654) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.641) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.646) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.657) total time=   0.6s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.655) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.660) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.809, test=0.660) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.621) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.660) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.654) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.652) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.644) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.636) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.808, test=0.656) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.659) total time=   0.8s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.661) total time=   0.6s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.826, test=0.662) total time=   0.6s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.624) total time=   0.6s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.659) total time=   0.5s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.657) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.656) total time=   0.6s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.645) total time=   0.7s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.635) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.824, test=0.657) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.852, test=0.663) total time=   0.9s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.850, test=0.662) total time=   0.9s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.664) total time=   0.9s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.852, test=0.628) total time=   0.9s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.659) total time=   0.9s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.850, test=0.658) total time=   0.9s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.852, test=0.657) total time=   0.9s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.646) total time=   1.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.851, test=0.641) total time=   0.9s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.659) total time=   0.9s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.658) total time=   0.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.654) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.624) total time=   0.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.656) total time=   0.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.661) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.625) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.654) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.628) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.649) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.643) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.664) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.655) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.663) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.633) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.658) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.652) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.649) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.658) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.665) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.630) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.660) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.655) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.638) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.643) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.654) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.662) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.666) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.634) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.664) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.660) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.641) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.656) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.662) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.660) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.666) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.641) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.724, test=0.661) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.659) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.659) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.645) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.651) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.659) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.652) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.656) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.845, test=0.652) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.618) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.851, test=0.661) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.652) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.855, test=0.642) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.648) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.856, test=0.634) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.648) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.895, test=0.652) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.653) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.653) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.895, test=0.618) total time=   0.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.659) total time=   0.5s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.895, test=0.652) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.898, test=0.642) total time=   0.6s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.892, test=0.648) total time=   0.8s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.895, test=0.634) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.653) total time=   0.5s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.941, test=0.646) total time=   0.9s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.939, test=0.647) total time=   0.9s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.647) total time=   0.8s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.942, test=0.616) total time=   0.9s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.948, test=0.656) total time=   0.9s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.944, test=0.644) total time=   1.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.949, test=0.636) total time=   0.9s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.943, test=0.645) total time=   0.9s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.630) total time=   1.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.936, test=0.653) total time=   1.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.655) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.648) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.621) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.653) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.660) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.623) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.644) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.654) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.621) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.644) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.623) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.642) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.654) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.624) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.662) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.644) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.661) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.660) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.661) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.628) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.662) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.637) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.652) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.660) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.661) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.666) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.631) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.662) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.665) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.639) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.643) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.654) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.664) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.662) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.668) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.635) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.661) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.666) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.654) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.641) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.646) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.657) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.809, test=0.654) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.661) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.806, test=0.659) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.811, test=0.622) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.661) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.809, test=0.654) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.652) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.644) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.637) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.806, test=0.655) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.659) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.826, test=0.661) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.823, test=0.661) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.624) total time=   0.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.825, test=0.660) total time=   0.6s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.825, test=0.658) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.656) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.826, test=0.644) total time=   0.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.825, test=0.635) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.822, test=0.658) total time=   0.5s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.663) total time=   1.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.662) total time=   1.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.663) total time=   1.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.628) total time=   1.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.658) total time=   1.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.658) total time=   0.9s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.850, test=0.656) total time=   1.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.645) total time=   0.9s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.641) total time=   0.9s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.844, test=0.660) total time=   0.9s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.658) total time=   0.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.654) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.624) total time=   0.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.656) total time=   0.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.661) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.625) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.628) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.649) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.643) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.664) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.656) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.663) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.633) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.657) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.653) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.649) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.659) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.665) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.629) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.660) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.656) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.638) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.655) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.661) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.663) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.666) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.634) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.663) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.662) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.661) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.640) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.645) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.656) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.663) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.661) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.667) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.729, test=0.638) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.724, test=0.661) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.660) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.661) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.644) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.650) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.660) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.653) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.847, test=0.656) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.841, test=0.652) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.620) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.658) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.650) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.645) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.647) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.636) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.846, test=0.653) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.890, test=0.655) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.887, test=0.652) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.883, test=0.652) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.893, test=0.622) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.892, test=0.656) total time=   0.5s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.893, test=0.650) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.894, test=0.645) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.651) total time=   0.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.892, test=0.634) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.888, test=0.655) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.645) total time=   0.8s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.937, test=0.647) total time=   0.8s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.933, test=0.645) total time=   0.8s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.939, test=0.619) total time=   0.8s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.943, test=0.654) total time=   0.8s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.939, test=0.646) total time=   0.9s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.944, test=0.642) total time=   0.8s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.939, test=0.650) total time=   1.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.937, test=0.633) total time=   0.8s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.654) total time=   0.7s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.656) total time=   0.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.621) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.652) total time=   0.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.660) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.623) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.644) total time=   0.0s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.654) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.621) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.661) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.644) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.623) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.655) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.624) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.662) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.626) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.644) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.660) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.676, test=0.661) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.628) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.676, test=0.662) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.638) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.652) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.661) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.680, test=0.666) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.630) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.663) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.665) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.654) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.639) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.643) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.654) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.663) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.663) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.687, test=0.667) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.635) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.663) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.666) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.654) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.641) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.646) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.657) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.793, test=0.657) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.795, test=0.659) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.789, test=0.659) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.793, test=0.626) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.793, test=0.661) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.794, test=0.655) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.797, test=0.655) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.794, test=0.648) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.794, test=0.638) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.788, test=0.657) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.662) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.660) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.805, test=0.663) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.809, test=0.627) total time=   0.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.807, test=0.659) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.659) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.811, test=0.656) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.648) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.637) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.803, test=0.658) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.829, test=0.663) total time=   0.8s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.828, test=0.661) total time=   0.9s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.825, test=0.664) total time=   0.9s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.829, test=0.632) total time=   0.8s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.828, test=0.660) total time=   0.8s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.827, test=0.660) total time=   0.8s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.830, test=0.657) total time=   0.9s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.826, test=0.648) total time=   0.9s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.828, test=0.643) total time=   0.9s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.824, test=0.660) total time=   0.8s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.658) total time=   0.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.654) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.624) total time=   0.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.656) total time=   0.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.661) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.625) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.656) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.649) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.643) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.664) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.656) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.664) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.658) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.652) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.634) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.649) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.660) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.663) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.630) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.661) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.640) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.655) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.661) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.663) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.698, test=0.664) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.634) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.664) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.663) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.659) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.642) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.646) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.656) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.722, test=0.664) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.662) total time=   0.6s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.719, test=0.665) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.722, test=0.639) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.718, test=0.662) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.662) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.658) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.647) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.649) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.722, test=0.657) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.660) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.659) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.825, test=0.657) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.621) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.660) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.650) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.833, test=0.654) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.643) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.639) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.653) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.870, test=0.658) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.866, test=0.658) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.863, test=0.655) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.871, test=0.625) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.871, test=0.659) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.870, test=0.651) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.873, test=0.650) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.868, test=0.646) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.872, test=0.639) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.867, test=0.655) total time=   0.5s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.917, test=0.656) total time=   0.8s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.916, test=0.651) total time=   0.8s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.912, test=0.651) total time=   0.8s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.916, test=0.620) total time=   0.8s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.920, test=0.657) total time=   0.9s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.919, test=0.650) total time=   0.9s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.925, test=0.646) total time=   0.8s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.917, test=0.647) total time=   0.8s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.919, test=0.638) total time=   0.9s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.912, test=0.655) total time=   0.8s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.651) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.651) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.646) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.619) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.648) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.657) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.640) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.620) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.639) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.638) total time=   0.0s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.652) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.644) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.618) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.653) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.660) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.642) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.620) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.641) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.654) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.647) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.622) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.654) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.657) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.644) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.649, test=0.620) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.640) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.641) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.651) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.666, test=0.661) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.626) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.655) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.659) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.647) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.632) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.639) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.662) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.627) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.657) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.660) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.649) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.633) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.640) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.649) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.660) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.656) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.665) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.629) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.660) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.663) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.649) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.634) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.641) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.651) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.660) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.651) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.809, test=0.651) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.823, test=0.614) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.651) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.819, test=0.651) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.638) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.819, test=0.633) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.817, test=0.632) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.655) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.832, test=0.661) total time=   0.7s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.832, test=0.651) total time=   0.6s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.652) total time=   0.6s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.841, test=0.617) total time=   0.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.835, test=0.652) total time=   0.7s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.835, test=0.651) total time=   0.7s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.642) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.835, test=0.635) total time=   0.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.835, test=0.631) total time=   0.6s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.832, test=0.655) total time=   0.7s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.662) total time=   1.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.650) total time=   1.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.856, test=0.656) total time=   1.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.619) total time=   1.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.861, test=0.648) total time=   1.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.649) total time=   1.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.643) total time=   1.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.858, test=0.636) total time=   1.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.863, test=0.631) total time=   1.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.857, test=0.654) total time=   1.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.654) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.647) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.624) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.650) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.657) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.644) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.620) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.640) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time=   0.0s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.652) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.626) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.648) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.626) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.661) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.655) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.664) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.631) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.660) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.653) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.636) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.648) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.650) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.659) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.664) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.631) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.660) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.650) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.636) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.641) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.649) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.662) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.666) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.636) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.659) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.666) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.652) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.641) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.643) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.652) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.741, test=0.662) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.663) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.666) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.741, test=0.640) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.659) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.663) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.742, test=0.652) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.645) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.643) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.653) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.873, test=0.654) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.649) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.866, test=0.646) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.612) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.651) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.645) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.876, test=0.646) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.863, test=0.635) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.631) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.868, test=0.651) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.916, test=0.649) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.920, test=0.647) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.915, test=0.647) total time=   0.6s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.914, test=0.610) total time=   0.7s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.910, test=0.649) total time=   0.6s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.916, test=0.641) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.921, test=0.644) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.638) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.920, test=0.633) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.909, test=0.647) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.963, test=0.644) total time=   1.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.965, test=0.640) total time=   0.9s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.966, test=0.639) total time=   0.9s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.961, test=0.603) total time=   0.8s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.643) total time=   0.8s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.964, test=0.637) total time=   0.9s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.968, test=0.639) total time=   0.9s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.964, test=0.636) total time=   0.8s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.966, test=0.633) total time=   0.8s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.961, test=0.643) total time=   0.8s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.651) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.651) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.646) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.619) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.648) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.657) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.620) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.639) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.638) total time=   0.0s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.652) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.644) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.618) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.653) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.660) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.642) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.620) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.641) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.654) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.647) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.622) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.654) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.657) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.644) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.649, test=0.620) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.641) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.641) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.651) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.667, test=0.661) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.626) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.655) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.659) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.647) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.632) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.647) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.662) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.627) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.657) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.660) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.649) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.633) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.640) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.649) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.661) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.656) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.665) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.628) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.660) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.663) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.649) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.635) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.641) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.651) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.660) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.650) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.808, test=0.652) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.822, test=0.613) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.817, test=0.652) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.651) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.819, test=0.638) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.633) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.632) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.657) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.660) total time=   0.6s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.651) total time=   0.6s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.826, test=0.653) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.839, test=0.617) total time=   0.6s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.834, test=0.652) total time=   0.6s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.833, test=0.653) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.642) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.833, test=0.636) total time=   0.6s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.833, test=0.631) total time=   0.6s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.656) total time=   0.6s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.857, test=0.661) total time=   1.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.857, test=0.651) total time=   1.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.854, test=0.656) total time=   1.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.619) total time=   1.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.649) total time=   1.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.860, test=0.651) total time=   1.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.642) total time=   1.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.855, test=0.636) total time=   1.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.860, test=0.632) total time=   1.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.855, test=0.655) total time=   1.0s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.654) total time=   0.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.647) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.649) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.624) total time=   0.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.651) total time=   0.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.657) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.644) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.620) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.640) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.653) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.627) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.648) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.627) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.662) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.656) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.664) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.633) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.659) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.653) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.634) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.649) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.650) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.659) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.664) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.629) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.661) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.664) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.650) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.636) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.650) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.662) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.662) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.667) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.632) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.660) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.653) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.641) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.654) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.664) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.664) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.667) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.742, test=0.637) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.661) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.664) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.741, test=0.654) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.646) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.647) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.656) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.653) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.866, test=0.652) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.862, test=0.647) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.867, test=0.614) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.649) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.645) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.873, test=0.640) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.863, test=0.638) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.867, test=0.634) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.862, test=0.649) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.918, test=0.650) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.919, test=0.644) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.907, test=0.645) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.907, test=0.614) total time=   0.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.646) total time=   0.5s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.914, test=0.640) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.916, test=0.642) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.904, test=0.642) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.911, test=0.636) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.904, test=0.648) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.959, test=0.644) total time=   0.9s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.964, test=0.639) total time=   0.9s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.961, test=0.645) total time=   0.7s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.609) total time=   0.8s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.962, test=0.643) total time=   0.9s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.962, test=0.632) total time=   0.8s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.960, test=0.637) total time=   0.8s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.959, test=0.641) total time=   0.8s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.960, test=0.634) total time=   0.8s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.952, test=0.645) total time=   0.8s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.651) total time=   0.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.651) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.646) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.619) total time=   0.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.648) total time=   0.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.657) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.620) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.639) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.638) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.652) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.644) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.618) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.653) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.659) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.642) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.620) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.641) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.654) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.647) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.622) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.654) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.657) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.644) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.649, test=0.620) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.640) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.641) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.650) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.666, test=0.660) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.626) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.667, test=0.654) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.659) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.647) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.631) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.639) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.647) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.650) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.671, test=0.662) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.627) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.658) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.660) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.648) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.632) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.640) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.649) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.679, test=0.660) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.655) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.679, test=0.665) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.629) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.660) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.663) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.648) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.635) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.641) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.650) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.662) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.803, test=0.654) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.798, test=0.654) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.615) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.805, test=0.653) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.806, test=0.656) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.809, test=0.640) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.806, test=0.634) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.637) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.652) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.816, test=0.661) total time=   0.6s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.818, test=0.653) total time=   0.6s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.815, test=0.655) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.826, test=0.618) total time=   0.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.821, test=0.652) total time=   0.5s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.821, test=0.654) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.823, test=0.642) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.635) total time=   0.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.635) total time=   0.6s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.654) total time=   0.6s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.841, test=0.662) total time=   0.9s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.841, test=0.653) total time=   1.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.838, test=0.658) total time=   1.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.620) total time=   1.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.844, test=0.653) total time=   1.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.654) total time=   1.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.643) total time=   1.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.838, test=0.637) total time=   1.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.635) total time=   1.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.841, test=0.655) total time=   1.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.654) total time=   0.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.647) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.624) total time=   0.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.652) total time=   0.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.657) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.643) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.619) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.640) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time=   0.0s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.658) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.652) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.626) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.648) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.627) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.644) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.662) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.656) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.663) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.631) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.660) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.666) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.653) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.634) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.649) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.649) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.657) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.663) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.632) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.663) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.661) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.650) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.638) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.641) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.650) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.661) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.660) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.667) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.632) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.662) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.666) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.652) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.642) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.645) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.656) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.662) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.659) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.666) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.633) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.662) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.663) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.653) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.646) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.729, test=0.647) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.658) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.658) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.846, test=0.656) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.844, test=0.654) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.618) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.855, test=0.652) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.651) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.856, test=0.647) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.845, test=0.640) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.634) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.649) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.892, test=0.655) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.887, test=0.651) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.888, test=0.652) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.886, test=0.615) total time=   0.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.888, test=0.651) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.891, test=0.646) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.895, test=0.646) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.882, test=0.644) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.892, test=0.634) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.888, test=0.649) total time=   0.5s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.940, test=0.651) total time=   0.8s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.937, test=0.648) total time=   0.8s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.646) total time=   0.8s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.937, test=0.615) total time=   0.8s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.647) total time=   0.8s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.940, test=0.640) total time=   0.8s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.644) total time=   0.9s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.645) total time=   0.8s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.634) total time=   0.8s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.650) total time=   0.9s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.656) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.622) total time=   0.0s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.653) total time=   0.0s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.660) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.623) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.644) total time=   0.0s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.653) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.621) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.644) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.623) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.642) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.645) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.659) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.655) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.655) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.624) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.662) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.626) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.644) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.659) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.659) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.676, test=0.661) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.627) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.676, test=0.661) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.664) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.654) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.638) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.645) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.653) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.660) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.662) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.680, test=0.665) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.630) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.663) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.665) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.654) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.639) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.643) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.654) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.663) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.663) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.667) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.635) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.662) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.666) total time=   0.4s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.655) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.640) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.646) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.657) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.784, test=0.659) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.786, test=0.662) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.781, test=0.662) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.785, test=0.625) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.785, test=0.661) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.786, test=0.658) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.787, test=0.656) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.786, test=0.646) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.786, test=0.639) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.780, test=0.658) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.799, test=0.661) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.799, test=0.661) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.796, test=0.663) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.800, test=0.628) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.798, test=0.659) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.799, test=0.660) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.801, test=0.656) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.799, test=0.646) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.798, test=0.636) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.795, test=0.659) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.819, test=0.662) total time=   0.9s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.818, test=0.662) total time=   0.8s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.815, test=0.665) total time=   0.9s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.820, test=0.632) total time=   0.9s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.818, test=0.660) total time=   0.8s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.818, test=0.661) total time=   0.8s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.820, test=0.656) total time=   0.9s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.817, test=0.645) total time=   0.9s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.819, test=0.642) total time=   0.9s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.815, test=0.660) total time=   0.8s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.658) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.655) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.654) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.624) total time=   0.0s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.656) total time=   0.0s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.662) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.625) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.644) total time=   0.0s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.660) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.655) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.628) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.663) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.648) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.628) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.644) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.661, test=0.663) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.656) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.664) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.658) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.667) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.651) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.634) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.649) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.659) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.660) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.664) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.629) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.660) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.653) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.639) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.644) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.655) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.660) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.663) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.699, test=0.664) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.634) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.662) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.664) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.657) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.643) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.646) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.699, test=0.656) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.724, test=0.663) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.662) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.664) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.724, test=0.639) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.662) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.723, test=0.664) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.723, test=0.659) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.647) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.722, test=0.649) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.658) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.820, test=0.658) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.658) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.660) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.822, test=0.623) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.661) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.822, test=0.653) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.825, test=0.650) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.642) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.825, test=0.637) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.656) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.860, test=0.658) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.861, test=0.656) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.857, test=0.657) total time=   0.5s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.863, test=0.626) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.863, test=0.658) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.863, test=0.654) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.864, test=0.648) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.859, test=0.645) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.865, test=0.636) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.856, test=0.658) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.917, test=0.651) total time=   0.9s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.916, test=0.650) total time=   0.9s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.913, test=0.652) total time=   0.9s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.918, test=0.622) total time=   0.8s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.919, test=0.656) total time=   0.8s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.919, test=0.649) total time=   0.9s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.918, test=0.647) total time=   0.9s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.912, test=0.645) total time=   0.8s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.915, test=0.635) total time=   0.8s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.913, test=0.658) total time=   0.8s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.656) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.622) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.653) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.660) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.623) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.644) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.653) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.621) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.644) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.623) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.642) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.645) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.659) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.655) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.655) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.624) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.662) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.626) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.644) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.659) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.659) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.676, test=0.661) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.628) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.676, test=0.661) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.664) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.654) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.637) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.644) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.653) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.660) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.662) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.680, test=0.665) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.631) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.663) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.665) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.654) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.639) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.643) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.654) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.663) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.664) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.667) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.635) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.662) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.687, test=0.666) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.655) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.641) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.646) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.657) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.782, test=0.659) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.784, test=0.661) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.779, test=0.661) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.783, test=0.625) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.782, test=0.661) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.783, test=0.659) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.785, test=0.656) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.785, test=0.645) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.784, test=0.639) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.778, test=0.659) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.796, test=0.660) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.797, test=0.661) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.794, test=0.662) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.797, test=0.628) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.796, test=0.660) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.796, test=0.662) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.799, test=0.655) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.797, test=0.646) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.795, test=0.636) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.793, test=0.659) total time=   0.5s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.816, test=0.663) total time=   1.0s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.816, test=0.662) total time=   0.8s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.813, test=0.665) total time=   0.9s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.817, test=0.632) total time=   0.9s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.815, test=0.660) total time=   0.8s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.816, test=0.662) total time=   0.9s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.818, test=0.656) total time=   0.9s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.815, test=0.646) total time=   0.8s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.816, test=0.642) total time=   0.9s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.812, test=0.660) total time=   0.8s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.658) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.655) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.654) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.624) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.656) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.662) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.645) total time=   0.0s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.625) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.0s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.660) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.655) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.628) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.663) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.649) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.629) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.644) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.661, test=0.663) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.657) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.664) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.659) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.667) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.651) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.634) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.648) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.658) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.661) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.664) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.630) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.660) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.653) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.639) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.644) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.655) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.660) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.699, test=0.664) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.698, test=0.665) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.634) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.662) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.664) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.657) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.643) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.646) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.698, test=0.656) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.722, test=0.662) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.663) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.667) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.724, test=0.638) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.719, test=0.662) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.664) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.722, test=0.658) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.647) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.650) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.658) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.658) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.658) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.659) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.820, test=0.624) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.660) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.820, test=0.655) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.822, test=0.648) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.819, test=0.643) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.822, test=0.636) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.657) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.857, test=0.657) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.858, test=0.656) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.854, test=0.659) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.861, test=0.625) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.860, test=0.658) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.860, test=0.654) total time=   0.4s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.862, test=0.647) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.856, test=0.644) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.863, test=0.634) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.855, test=0.659) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.913, test=0.652) total time=   0.9s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.911, test=0.652) total time=   0.8s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.909, test=0.655) total time=   0.8s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.916, test=0.621) total time=   0.9s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.915, test=0.654) total time=   0.8s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.916, test=0.650) total time=   0.8s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.915, test=0.644) total time=   0.8s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.910, test=0.645) total time=   0.8s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.914, test=0.634) total time=   0.8s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.909, test=0.658) total time=   0.8s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.656) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.621) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.652) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.660) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.623) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.644) total time=   0.0s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.653) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.621) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.644) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.623) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.642) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.645) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.659) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.654) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.655) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.624) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.662) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.626) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.644) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.676, test=0.659) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.660) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.675, test=0.661) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.627) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.675, test=0.661) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.676, test=0.664) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.654) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.638) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.645) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.652) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.680, test=0.660) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.662) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.679, test=0.665) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.630) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.680, test=0.663) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.680, test=0.665) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.653) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.639) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.643) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.680, test=0.654) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.663) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.687, test=0.663) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.667) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.635) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.663) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.666) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.687, test=0.654) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.641) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.687, test=0.646) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.656) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.767, test=0.659) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.769, test=0.658) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.764, test=0.659) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.769, test=0.627) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.769, test=0.661) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.768, test=0.659) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.770, test=0.654) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.770, test=0.644) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.768, test=0.640) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.765, test=0.658) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.780, test=0.661) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.781, test=0.660) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.778, test=0.662) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.781, test=0.629) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.781, test=0.660) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.781, test=0.662) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.783, test=0.654) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.781, test=0.645) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.780, test=0.638) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.778, test=0.660) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.799, test=0.663) total time=   0.8s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.800, test=0.662) total time=   1.0s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.795, test=0.664) total time=   0.8s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.800, test=0.634) total time=   0.9s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.799, test=0.661) total time=   0.9s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.799, test=0.663) total time=   0.8s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.800, test=0.656) total time=   0.8s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.799, test=0.646) total time=   0.8s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.799, test=0.643) total time=   1.0s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.796, test=0.661) total time=   1.0s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.658) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.655) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.654) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.624) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.656) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.645) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.625) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.661) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.655) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.658) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.663) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.649) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.628) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.644) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.664) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.657) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.663) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.633) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.659) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.667) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.652) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.634) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.649) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.659) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.661) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.665) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.631) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.660) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.654) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.639) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.644) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.656) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.698, test=0.660) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.698, test=0.664) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.696, test=0.666) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.636) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.698, test=0.663) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.698, test=0.664) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.699, test=0.658) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.698, test=0.644) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.699, test=0.646) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.697, test=0.657) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.718, test=0.663) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.717, test=0.662) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.717, test=0.666) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.640) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.715, test=0.662) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.717, test=0.663) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.718, test=0.660) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.718, test=0.648) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.718, test=0.650) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.717, test=0.660) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.800, test=0.659) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.803, test=0.657) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.796, test=0.661) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.625) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.801, test=0.664) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.655) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.806, test=0.650) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.805, test=0.643) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.805, test=0.637) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.800, test=0.655) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.657) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.655) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.834, test=0.660) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.628) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.841, test=0.664) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.657) total time=   0.7s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.843, test=0.649) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.839, test=0.647) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.637) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.837, test=0.658) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.892, test=0.653) total time=   0.8s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.892, test=0.653) total time=   0.9s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.890, test=0.657) total time=   0.8s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.898, test=0.627) total time=   0.8s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.894, test=0.659) total time=   0.8s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.894, test=0.652) total time=   0.9s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.897, test=0.645) total time=   1.0s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.893, test=0.648) total time=   0.8s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.895, test=0.637) total time=   0.8s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.892, test=0.659) total time=   0.8s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.651) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.651) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.645) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.619) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.648) total time=   0.0s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.656) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.640) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.620) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.639) total time=   0.0s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.638) total time=   0.0s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.652) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.644) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.618) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.653) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.659) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.642) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.620) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.640) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.641) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.654) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.647) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.621) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.653) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.657) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.644) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.649, test=0.620) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.640) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.641) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.657) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.650) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.666, test=0.660) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.626) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.667, test=0.655) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.659) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.648) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.631) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.640) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.646) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.659) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.650) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.671, test=0.661) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.627) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.658) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.660) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.648) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.632) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.640) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.649) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.679, test=0.660) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.654) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.679, test=0.664) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.628) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.660) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.663) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.648) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.635) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.641) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.651) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.660) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.657) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.797, test=0.653) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.808, test=0.617) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.803, test=0.653) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.653) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.807, test=0.641) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.637) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.803, test=0.634) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.655) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.817, test=0.660) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.654) total time=   0.6s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.815, test=0.655) total time=   0.5s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.824, test=0.619) total time=   0.6s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.653) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.821, test=0.654) total time=   0.8s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.822, test=0.642) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.638) total time=   0.6s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.634) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.655) total time=   0.5s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.661) total time=   1.0s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.844, test=0.654) total time=   1.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.842, test=0.656) total time=   1.0s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.620) total time=   1.5s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.651) total time=   1.5s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.654) total time=   1.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.644) total time=   1.4s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.637) total time=   1.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.635) total time=   1.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.844, test=0.655) total time=   1.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.655) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.647) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.623) total time=   0.0s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.652) total time=   0.0s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.657) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.643) total time=   0.0s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.619) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.639) total time=   0.0s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time=   0.0s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.652) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.627) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.663) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.647) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.626) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.662) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.657) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.663) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.632) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.667) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.652) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.636) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.649) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.650) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.660) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.657) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.663) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.628) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.661) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.649) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.637) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.642) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.650) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.662) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.659) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.666) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.632) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.659) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.666) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.652) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.644) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.645) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.655) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.663) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.663) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.665) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.634) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.658) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.662) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.655) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.648) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.647) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.656) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.856, test=0.653) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.851, test=0.651) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.653) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.855, test=0.618) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.858, test=0.647) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.857, test=0.644) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.858, test=0.644) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.851, test=0.639) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.858, test=0.631) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.650) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.652) total time=   0.6s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.901, test=0.650) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.899, test=0.654) total time=   0.6s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.900, test=0.615) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.899, test=0.647) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.903, test=0.642) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.907, test=0.647) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.898, test=0.642) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.906, test=0.632) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.900, test=0.653) total time=   0.5s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.962, test=0.647) total time=   1.0s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.957, test=0.645) total time=   0.9s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.647) total time=   0.9s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.959, test=0.610) total time=   0.9s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.955, test=0.643) total time=   0.9s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.636) total time=   0.9s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.961, test=0.637) total time=   1.0s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.645) total time=   0.9s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.959, test=0.626) total time=   0.9s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.960, test=0.648) total time=   0.9s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.651) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.651) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.645) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.619) total time=   0.0s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.648) total time=   0.0s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.656) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.620) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.639) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.638) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.652) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.644) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.618) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.653) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.659) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.642) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.620) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.640) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.641) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.654) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.647) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.621) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.653) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.657) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.645) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.649, test=0.620) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.641) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.641) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.658) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.649) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.666, test=0.660) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.626) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.667, test=0.655) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.659) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.648) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.631) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.640) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.646) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.659) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.650) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.671, test=0.662) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.627) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.658) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.660) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.648) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.632) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.640) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.649) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.679, test=0.660) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.653) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.679, test=0.665) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.628) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.660) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.663) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.648) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.635) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.641) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.651) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.799, test=0.661) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.801, test=0.656) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.795, test=0.654) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.805, test=0.616) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.801, test=0.654) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.653) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.643) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.636) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.801, test=0.635) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.654) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.815, test=0.660) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.817, test=0.654) total time=   0.6s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.813, test=0.656) total time=   0.7s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.822, test=0.619) total time=   0.6s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.818, test=0.653) total time=   0.6s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.654) total time=   0.6s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.643) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.818, test=0.638) total time=   0.6s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.818, test=0.635) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.818, test=0.655) total time=   0.6s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.842, test=0.661) total time=   1.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.842, test=0.653) total time=   1.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.840, test=0.656) total time=   1.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.618) total time=   1.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.652) total time=   1.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.844, test=0.654) total time=   1.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.645) total time=   1.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.840, test=0.638) total time=   1.0s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.635) total time=   1.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.655) total time=   1.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.654) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.647) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.623) total time=   0.0s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.652) total time=   0.0s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.657) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.643) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.619) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.639) total time=   0.0s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time=   0.0s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.652) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.627) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.664) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.648) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.626) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.656) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.664) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.631) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.666) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.652) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.636) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.648) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.661) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.656) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.662) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.630) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.662) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.649) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.637) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.642) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.650) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.663) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.659) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.667) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.633) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.661) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.666) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.652) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.643) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.645) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.652) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.662) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.661) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.665) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.637) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.661) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.663) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.741, test=0.655) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.647) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.647) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.656) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.856, test=0.652) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.652) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.653) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.619) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.856, test=0.648) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.855, test=0.649) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.858, test=0.644) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.640) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.635) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.855, test=0.651) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.904, test=0.646) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.901, test=0.649) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.895, test=0.652) total time=   0.5s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.616) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.898, test=0.647) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.899, test=0.644) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.905, test=0.645) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.894, test=0.645) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.901, test=0.635) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.901, test=0.651) total time=   0.6s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.642) total time=   1.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.642) total time=   1.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.951, test=0.646) total time=   1.0s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.955, test=0.612) total time=   1.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.952, test=0.640) total time=   1.0s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.955, test=0.638) total time=   0.9s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.960, test=0.638) total time=   1.0s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.644) total time=   0.9s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.632) total time=   1.0s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.962, test=0.646) total time=   1.0s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.651) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.651) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.645) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.619) total time=   0.0s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.648) total time=   0.0s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.656) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.620) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.639) total time=   0.0s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time=   0.0s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.652) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.644) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.618) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.653) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.659) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.642) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.620) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.640) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.641) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.654) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.647) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.621) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.654) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.657) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.645) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.620) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.640) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.641) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.667, test=0.658) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.649) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.666, test=0.660) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.627) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.667, test=0.656) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.667, test=0.659) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.648) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.631) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.639) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.667, test=0.647) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.671, test=0.660) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.650) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.670, test=0.662) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.628) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.671, test=0.658) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.671, test=0.660) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.649) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.632) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.640) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.649) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.678, test=0.660) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.654) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.678, test=0.665) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.628) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.678, test=0.661) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.679, test=0.663) total time=   0.4s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.648) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.636) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.640) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.651) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.787, test=0.662) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.786, test=0.656) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.781, test=0.660) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.791, test=0.619) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.788, test=0.655) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.789, test=0.655) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.791, test=0.644) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.789, test=0.635) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.788, test=0.637) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.788, test=0.655) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.801, test=0.661) total time=   0.8s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.804, test=0.655) total time=   0.7s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.799, test=0.659) total time=   0.7s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.621) total time=   0.6s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.803, test=0.656) total time=   0.7s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.805, test=0.656) total time=   0.6s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.806, test=0.644) total time=   0.7s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.803, test=0.637) total time=   0.6s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.804, test=0.637) total time=   0.7s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.802, test=0.657) total time=   0.5s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.826, test=0.662) total time=   1.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.828, test=0.654) total time=   1.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.823, test=0.659) total time=   1.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.831, test=0.622) total time=   1.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.829, test=0.653) total time=   1.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.828, test=0.656) total time=   1.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.831, test=0.645) total time=   1.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.823, test=0.638) total time=   1.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.830, test=0.635) total time=   1.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.826, test=0.657) total time=   1.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.654) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.647) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.647) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.624) total time=   0.0s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.652) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.657) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.643) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.619) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.639) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.641) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.652) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.655) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.628) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.663) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.647) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.627) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.656) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.664) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.632) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.662) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.667) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.636) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.649) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.649) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.660) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.656) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.663) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.631) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.662) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.648) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.637) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.641) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.650) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.661) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.660) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.668) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.634) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.699, test=0.663) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.666) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.652) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.644) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.645) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.655) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.662) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.661) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.666) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.634) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.660) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.663) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.656) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.647) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.646) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.658) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.838, test=0.656) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.836, test=0.650) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.833, test=0.657) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.838, test=0.616) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.838, test=0.652) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.839, test=0.654) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.841, test=0.645) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.644) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.838, test=0.633) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.836, test=0.649) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.885, test=0.653) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.879, test=0.650) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.876, test=0.654) total time=   0.5s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.880, test=0.615) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.878, test=0.652) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.880, test=0.651) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.884, test=0.645) total time=   0.6s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.873, test=0.646) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.880, test=0.630) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.877, test=0.649) total time=   0.6s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.940, test=0.650) total time=   0.8s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.936, test=0.648) total time=   0.9s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.931, test=0.647) total time=   0.9s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.939, test=0.613) total time=   0.8s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.928, test=0.647) total time=   0.9s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.936, test=0.643) total time=   0.9s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.944, test=0.635) total time=   0.9s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.931, test=0.646) total time=   1.0s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.629) total time=   0.9s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.930, test=0.646) total time=   0.8s
C:\Users\woowe\anaconda\Lib\site-packages\numpy\ma\core.py:2820: RuntimeWarning: invalid value encountered in cast
  _data = np.array(data, dtype=dtype, copy=copy,
Out[108]:
GridSearchCV(cv=KFold(n_splits=10, random_state=42, shuffle=True),
             estimator=XGBClassifier(base_score=None, booster=None,
                                     callbacks=None, colsample_bylevel=None,
                                     colsample_bynode=None,
                                     colsample_bytree=None, device=None,
                                     early_stopping_rounds=None,
                                     enable_categorical=False, eval_metric=None,
                                     feature_types=None, gamma=None,
                                     grow_policy=None, importance_type=None,
                                     in...
                                     max_leaves=None, min_child_weight=None,
                                     missing=nan, monotone_constraints=None,
                                     multi_strategy=None, n_estimators=None,
                                     n_jobs=None, num_parallel_tree=None,
                                     random_state=42, ...),
             param_grid={'alpha': [0, 0.1, 1], 'colsample_bytree': [0.3, 0.7],
                         'lambda': [0, 0.1, 1], 'learning_rate': [0.01, 0.05],
                         'max_depth': [2, 5, 10],
                         'n_estimators': [50, 100, 200]},
             return_train_score=True, scoring='roc_auc', verbose=4)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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GridSearchCV(cv=KFold(n_splits=10, random_state=42, shuffle=True),
             estimator=XGBClassifier(base_score=None, booster=None,
                                     callbacks=None, colsample_bylevel=None,
                                     colsample_bynode=None,
                                     colsample_bytree=None, device=None,
                                     early_stopping_rounds=None,
                                     enable_categorical=False, eval_metric=None,
                                     feature_types=None, gamma=None,
                                     grow_policy=None, importance_type=None,
                                     in...
                                     max_leaves=None, min_child_weight=None,
                                     missing=nan, monotone_constraints=None,
                                     multi_strategy=None, n_estimators=None,
                                     n_jobs=None, num_parallel_tree=None,
                                     random_state=42, ...),
             param_grid={'alpha': [0, 0.1, 1], 'colsample_bytree': [0.3, 0.7],
                         'lambda': [0, 0.1, 1], 'learning_rate': [0.01, 0.05],
                         'max_depth': [2, 5, 10],
                         'n_estimators': [50, 100, 200]},
             return_train_score=True, scoring='roc_auc', verbose=4)
XGBClassifier(alpha=1, base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=0.3, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, lambda=1, learning_rate=0.05,
              max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=5, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=200, n_jobs=None, ...)
XGBClassifier(alpha=1, base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=0.3, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, lambda=1, learning_rate=0.05,
              max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=5, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=200, n_jobs=None, ...)

Model Performance¶

In [110]:
report_GridSearchCV_results(grid_search_xgb)
- Best combination of hyperparams:
 {'alpha': 1, 'colsample_bytree': 0.3, 'lambda': 1, 'learning_rate': 0.05, 'max_depth': 5, 'n_estimators': 200} 

- Best mean_test_score:
 0.6574395398994423 

- Score by fold for best estimator:
 [0.6630527191725473, 0.6624372683405841, 0.6663100724985688, 0.6403077003405951, 0.6622020538423473, 0.6628095450687967, 0.6601580040413113, 0.6475613409193599, 0.649832697452752, 0.6597239973175595] 

- Top 10 hyperparameter combinations by mean_test_score:
mean_test_score param_colsample_bytree param_n_estimators param_max_depth param_alpha param_lambda param_learning_rate
rank_test_score
1 0.657440 0.3 200 5 1.0 1.0 0.05
2 0.656978 0.3 200 5 1.0 0.1 0.05
3 0.656880 0.3 200 5 1.0 0.0 0.05
4 0.656737 0.3 200 5 0.0 0.0 0.05
5 0.656657 0.3 200 5 0.0 0.1 0.05
6 0.656594 0.3 200 5 0.1 1.0 0.05
7 0.656527 0.3 200 5 0.0 1.0 0.05
8 0.656452 0.3 200 5 0.1 0.0 0.05
9 0.656324 0.3 200 5 0.1 0.1 0.05
10 0.655983 0.7 200 5 0.1 0.1 0.05
In [111]:
compare_performance(grid_search_xgb)
Out[111]:
train_AUC val_AUC
1 0.648459 0.644285
2 0.650600 0.646056
3 0.652415 0.647117
4 0.679256 0.652434
5 0.684090 0.653665
6 0.690959 0.655600
7 0.813134 0.649548
8 0.829279 0.651748
9 0.851257 0.653650
10 0.651624 0.646783
Mean 0.715107 0.650089
In [112]:
best_model_xgb=grid_search_xgb.best_estimator_
In [113]:
plot_probability_std(best_model_xgb, df_train, y_name, x_name, kf10, "XGBoost")
No description has been provided for this image
In [114]:
plot_avg_feature_importance(best_model_xgb, df_train, y_name, x_name, kf10, "XGBoost")
No description has been provided for this image
In [115]:
evaluate_model(best_model_xgb, df_X_test_scaled, df_y_test)
Test AUC: 0.66
Accuracy: 0.62
Confusion Matrix:
[[1984  674]
 [1216 1126]]
No description has been provided for this image
Classification Report:
              precision    recall  f1-score   support

           0       0.62      0.75      0.68      2658
           1       0.63      0.48      0.54      2342

    accuracy                           0.62      5000
   macro avg       0.62      0.61      0.61      5000
weighted avg       0.62      0.62      0.61      5000

In [116]:
plot_roc_curve(best_model_xgb, df_X_test_scaled, df_y_test)
No description has been provided for this image
In [ ]: